text stringlengths 17 3.36M | source stringlengths 3 333 | __index_level_0__ int64 0 518k |
|---|---|---|
We demonstrate the possibility of coding parts, features that are higher level than boundaries, using a modified AT field after augmenting the interaction term of the AT energy with a non-local term and weakening the separation into boundary/not-boundary phases. The iteratively extracted parts using the level curves with double point singularities are organized as a proper binary tree. Inconsistencies due to non-generic configurations for level curves as well as due to visual changes such as occlusion are successfully handled once the tree is endowed with a probabilistic structure. The work is a step in establishing the AT function as a bridge between low and high level visual processing. | From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree | 3,300 |
Perception research provides strong evidence in favor of part based representation of shapes in human visual system. Despite considerable differences among different theories in terms of how part boundaries are found, there is substantial agreement on that the process depends on many local and global geometric factors. This poses an important challenge from the computational point of view. In the first part of the chapter, I present a novel decomposition method by taking both local and global interactions within the shape domain into account. At the top of the partitioning hierarchy, the shape gets split into two parts capturing, respectively, the gross structure and the peripheral structure. The gross structure may be conceived as the least deformable part of the shape which remains stable under visual transformations. The peripheral structure includes limbs, protrusions, and boundary texture. Such a separation is in accord with the behavior of the artists who start with a gross shape and enrich it with details. The method is particularly interesting from the computational point of view as it does not resort to any geometric notions (e.g. curvature, convexity) explicitly. In the second part of the chapter, I relate the new method to PDE based shape representation schemes. | Extracting Parts of 2D Shapes Using Local and Global Interactions
Simultaneously | 3,301 |
This paper presents a new axis-based shape representation scheme along with a matching framework to address the problem of generic shape recognition. The main idea is to define the relative spatial arrangement of local symmetry axes and their metric properties in a shape centered coordinate frame. The resulting descriptions are invariant to scale, rotation, small changes in viewpoint and articulations. Symmetry points are extracted from a surface whose level curves roughly mimic the motion by curvature. By increasing the amount of smoothing on the evolving curve, only those symmetry axes that correspond to the most prominent parts of a shape are extracted. The representation does not suffer from the common instability problems of the traditional connected skeletons. It captures the perceptual qualities of shapes well. Therefore finding the similarities and the differences among shapes becomes easier. The matching process gives highly successful results on a diverse database of 2D shapes. | An Axis-Based Representation for Recognition | 3,302 |
We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones. | Disconnected Skeleton: Shape at its Absolute Scale | 3,303 |
Despite the recent developments in spatiotemporal local features for action recognition in video sequences, local color information has so far been ignored. However, color has been proved an important element to the success of automated recognition of objects and scenes. In this paper we extend the space-time interest point descriptor STIP to take into account the color information on the features' neighborhood. We compare the performance of our color-aware version of STIP (which we have called HueSTIP) with the original one. | Hue Histograms to Spatiotemporal Local Features for Action Recognition | 3,304 |
We present a method for nonrigid registration of 2-D geometric shapes. Our contribution is twofold. First, we extend the classic chamfer-matching energy to a variational functional. Secondly, we introduce a meshless deformation model that can handle significant high-curvature deformations. We represent 2-D shapes implicitly using distance transforms, and registration error is defined based on the shape contours' mutual distances. In addition, we model global shape deformation as an approximation blended from local deformation fields using partition-of-unity. The global deformation field is regularized by penalizing inconsistencies between local fields. The representation can be made adaptive to shape's contour, leading to registration that is both flexible and efficient. Finally, registration is achieved by minimizing a variational chamfer-energy functional combined with the consistency regularizer. We demonstrate the effectiveness of our method on a number of experiments. | A Meshless Method for Variational Nonrigid 2-D Shape Registration | 3,305 |
Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods. | Curved Gabor Filters for Fingerprint Image Enhancement | 3,306 |
The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time. | Positive Semidefinite Metric Learning Using Boosting-like Algorithms | 3,307 |
We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task. | A supervised clustering approach for fMRI-based inference of brain
states | 3,308 |
Preoperative templating in Total Hip Replacement (THR) is a method to estimate the optimal size and position of the implant. Today, observational (manual) size recognition techniques are still used to find a suitable implant for the patient. Therefore, a digital and automated technique should be developed so that the implant size recognition process can be effectively implemented. For this purpose, we have introduced the new technique for acetabular implant size recognition in THR preoperative planning based on the diameter of acetabulum size. This technique enables the surgeon to recognise a digital acetabular implant size automatically. Ten randomly selected X-rays of unidentified patients were used to test the accuracy and utility of an automated implant size recognition technique. Based on the testing result, the new technique yielded very close results to those obtained by the observational method in nine studies (90%). | An Automated Size Recognition Technique for Acetabular Implant in Total
Hip Replacement | 3,309 |
This paper presents some experiments regarding applications development on high performance media processors included in Philips Nexperia Family. The PNX1302 dedicated DVB-T kit used has some limitations. Our work has succeeded to overcome these limitations and to make possible a general-purpose use of this kit. For exemplification two typical applications, important both for multimedia and DVB, are analyzed: MPEG2 video stream decoding and MP3 audio decoding. These original implementations are compared (in speed, memory requirements and costs) with Philips Nexperia Library. | Considerations and Results in Multimedia and DVB Application Development
on Philips Nexperia Platform | 3,310 |
Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person re-identification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances. | A Multiple Component Matching Framework for Person Re-Identification | 3,311 |
In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions. | Face Recognition using 3D Facial Shape and Color Map Information:
Comparison and Combination | 3,312 |
Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given. | Invariant Representative Cocycles of Cohomology Generators using
Irregular Graph Pyramids | 3,313 |
We give a new algorithmic solution to the well-known five-point relative pose problem. Our approach does not deal with the famous cubic constraint on an essential matrix. Instead, we use the Cayley representation of rotations in order to obtain a polynomial system from epipolar constraints. Solving that system, we directly get relative rotation and translation parameters of the cameras in terms of roots of a 10th degree polynomial. | An Algorithmic Solution to the Five-Point Pose Problem Based on the
Cayley Representation of Rotations | 3,314 |
This article describes JECT-OMR, a system that analyzes digital images representing scans of multiple-choice tests compiled by students. The system performs a structural analysis of the document in order to get the chosen answer for each question, and it also contains a bar-code decoder, used for the identification of additional information encoded in the document. JECT-OMR was implemented using the Python programming language, and leverages the power of the Gamera framework in order to accomplish its task. The system exhibits an accuracy of over 99% in the recognition of marked and non-marked squares representing answers, thus making it suitable for real world applications | A Multiple-Choice Test Recognition System based on the Gamera Framework | 3,315 |
Cohomology and cohomology ring of three-dimensional (3D) objects are topological invariants that characterize holes and their relations. Cohomology ring has been traditionally computed on simplicial complexes. Nevertheless, cubical complexes deal directly with the voxels in 3D images, no additional triangulation is necessary, facilitating efficient algorithms for the computation of topological invariants in the image context. In this paper, we present formulas to directly compute the cohomology ring of 3D cubical complexes without making use of any additional triangulation. Starting from a cubical complex $Q$ that represents a 3D binary-valued digital picture whose foreground has one connected component, we compute first the cohomological information on the boundary of the object, $\partial Q$ by an incremental technique; then, using a face reduction algorithm, we compute it on the whole object; finally, applying the mentioned formulas, the cohomology ring is computed from such information. | Cubical Cohomology Ring of 3D Photographs | 3,316 |
Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms. | Preprocessing for Automating Early Detection of Cervical Cancer | 3,317 |
We propose a method for computing the cohomology ring of three--dimensional (3D) digital binary-valued pictures. We obtain the cohomology ring of a 3D digital binary--valued picture $I$, via a simplicial complex K(I)topologically representing (up to isomorphisms of pictures) the picture I. The usefulness of a simplicial description of the "digital" cohomology ring of 3D digital binary-valued pictures is tested by means of a small program visualizing the different steps of the method. Some examples concerning topological thinning, the visualization of representative (co)cycles of (co)homology generators and the computation of the cup product on the cohomology of simple pictures are showed. | On the Cohomology of 3D Digital Images | 3,318 |
In this paper, we formalize the notion of lambda-AT-model (where $\lambda$ is a non-null integer) for a given chain complex, which allows the computation of homological information in the integer domain avoiding using the Smith Normal Form of the boundary matrices. We present an algorithm for computing such a model, obtaining Betti numbers, the prime numbers p involved in the invariant factors of the torsion subgroup of homology, the amount of invariant factors that are a power of p and a set of representative cycles of generators of homology mod p, for each p. Moreover, we establish the minimum valid lambda for such a construction, what cuts down the computational costs related to the torsion subgroup. The tools described here are useful to determine topological information of nD structured objects such as simplicial, cubical or simploidal complexes and are applicable to extract such an information from digital pictures. | A Tool for Integer Homology Computation: Lambda-At Model | 3,319 |
Image splicing is a common form of image forgery. Such alterations may leave no visual clues of tampering. In recent works camera characteristics consistency across the image has been used to establish the authenticity and integrity of digital images. Such constant camera characteristic properties are inherent from camera manufacturing processes and are unique. The majority of digital cameras are equipped with spherical lens and this introduces radial distortions on images. This aberration is often disturbed and fails to be consistent across the image, when an image is spliced. This paper describes the detection of splicing operation on images by estimating radial distortion from different portions of the image using line-based calibration. For the first time, the detection of image splicing through the verification of consistency of lens radial distortion has been explored in this paper. The conducted experiments demonstrate the efficacy of our proposed approach for the detection of image splicing on both synthetic and real images. | Image Splicing Detection Using Inherent Lens Radial Distortion | 3,320 |
It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of the neural network, i.e. the learning rate, activation function parameter, momentum number, the number of nodes in one layer, etc. we developed a strategy that can automatically select comparatively better combination of these parameters. We also introduce another way to perform back propagation. Instead of using the partial differential of the error function, we use optimal algorithm; namely Powell's direction set to minimize the error function. We were also interested in construction an authentic emotion databases. This is a very important task because nowadays there is no such database available. Finally, we perform several experiments and show that our neural network approach can be successfully used for emotion recognition. | Neural Networks for Emotion Classification | 3,321 |
This work discusses preliminary work aimed at simulating and visualizing the growth process of a tiny structure inside the cell---the microtubule. Difficulty of recording the process lies in the fact that the tissue preparation method for electronic microscopes is highly destructive to live cells. Here in this paper, our approach is to take pictures of microtubules at different time slots and then appropriately combine these images into a coherent video. Experimental results are given on real data. | Alignment of Microtubule Imagery | 3,322 |
Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods. | Incremental Top-k List Comparison Approach to Robust Multi-Structure
Model Fitting | 3,323 |
Topological alignments and snakes are used in image processing, particularly in locating object boundaries. Both of them have their own advantages and limitations. To improve the overall image boundary detection system, we focused on developing a novel algorithm for image processing. The algorithm we propose to develop will based on the active contour method in conjunction with topological alignments method to enhance the image detection approach. The algorithm presents novel technique to incorporate the advantages of both Topological Alignments and snakes. Where the initial segmentation by Topological Alignments is firstly transformed into the input of the snake model and begins its evolvement to the interested object boundary. The results show that the algorithm can deal with low contrast images and shape cells, demonstrate the segmentation accuracy under weak image boundaries, which responsible for lacking accuracy in image detecting techniques. We have achieved better segmentation and boundary detecting for the image, also the ability of the system to improve the low contrast and deal with over and under segmentation. | A Novel Image Segmentation Enhancement Technique based on Active Contour
and Topological Alignments | 3,324 |
Detection of geometric features in digital images is an important exercise in image analysis and computer vision. The Hough Transform techniques for detection of circles require a huge memory space for data processing hence requiring a lot of time in computing the locations of the data space, writing to and searching through the memory space. In this paper we propose a novel and efficient scheme for detecting circles in edge-detected grayscale digital images. We use Ant-system algorithm for this purpose which has not yet found much application in this field. The main feature of this scheme is that it can detect both intersecting as well as non-intersecting circles with a time efficiency that makes it useful in real time applications. We build up an ant system of new type which finds out closed loops in the image and then tests them for circles. | An efficient circle detection scheme in digital images using ant system
algorithm | 3,325 |
This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log- Gabor iris encoder. Twelve tests involving single/multienrollment and conducted on Bath Iris Image Database are presented here. One of these tests achieves an Equal Error Rate comparable to the lowest value reported so far for this database. New Matlab tools for iris image processing are also released together with this paper: a second version of the Circular Fuzzy Iris Segmentator (CFIS2), a fast Log-Gabor encoder and two Haar-Hilbert based encoders. | Comparing Haar-Hilbert and Log-Gabor Based Iris Encoders on Bath Iris
Image Database | 3,326 |
This paper presents a new algorithm to track mobile objects in different scene conditions. The main idea of the proposed tracker includes estimation, multi-features similarity measures and trajectory filtering. A feature set (distance, area, shape ratio, color histogram) is defined for each tracked object to search for the best matching object. Its best matching object and its state estimated by the Kalman filter are combined to update position and size of the tracked object. However, the mobile object trajectories are usually fragmented because of occlusions and misdetections. Therefore, we also propose a trajectory filtering, named global tracker, aims at removing the noisy trajectories and fusing the fragmented trajectories belonging to a same mobile object. The method has been tested with five videos of different scene conditions. Three of them are provided by the ETISEO benchmarking project (http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker performance has been compared with other seven tracking algorithms. The advantages of our approach over the existing state of the art ones are: (i) no prior knowledge information is required (e.g. no calibration and no contextual models are needed), (ii) the tracker is more reliable by combining multiple feature similarities, (iii) the tracker can perform in different scene conditions: single/several mobile objects, weak/strong illumination, indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art. | Robust Mobile Object Tracking Based on Multiple Feature Similarity and
Trajectory Filtering | 3,327 |
This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate. | Polar Fusion Technique Analysis for Evaluating the Performances of Image
Fusion of Thermal and Visual Images for Human Face Recognition | 3,328 |
This paper demonstrates two different fusion techniques at two different levels of a human face recognition process. The first one is called data fusion at lower level and the second one is the decision fusion towards the end of the recognition process. At first a data fusion is applied on visual and corresponding thermal images to generate fused image. Data fusion is implemented in the wavelet domain after decomposing the images through Daubechies wavelet coefficients (db2). During the data fusion maximum of approximate and other three details coefficients are merged together. After that Principle Component Analysis (PCA) is applied over the fused coefficients and finally two different artificial neural networks namely Multilayer Perceptron(MLP) and Radial Basis Function(RBF) networks have been used separately to classify the images. After that, for decision fusion based decisions from both the classifiers are combined together using Bayesian formulation. For experiments, IRIS thermal/visible Face Database has been used. Experimental results show that the performance of multiple classifier system along with decision fusion works well over the single classifier system. | Next Level of Data Fusion for Human Face Recognition | 3,329 |
In this paper, we present a technique by which high-intensity feature vectors extracted from the Gabor wavelet transformation of frontal face images, is combined together with Independent Component Analysis (ICA) for enhanced face recognition. Firstly, the high-intensity feature vectors are automatically extracted using the local characteristics of each individual face from the Gabor transformed images. Then ICA is applied on these locally extracted high-intensity feature vectors of the facial images to obtain the independent high intensity feature (IHIF) vectors. These IHIF forms the basis of the work. Finally, the image classification is done using these IHIF vectors, which are considered as representatives of the images. The importance behind implementing ICA along with the high-intensity features of Gabor wavelet transformation is twofold. On the one hand, selecting peaks of the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. Thus these images produce salient local features that are most suitable for face recognition. On the other hand, as the ICA employs locally salient features from the high informative facial parts, it reduces redundancy and represents independent features explicitly. These independent features are most useful for subsequent facial discrimination and associative recall. The efficiency of IHIF method is demonstrated by the experiment on frontal facial images dataset, selected from the FERET, FRAV2D, and the ORL database. | High Performance Human Face Recognition using Independent High Intensity
Gabor Wavelet Responses: A Statistical Approach | 3,330 |
Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Dominant local orientation angle {\theta} and Coherence are computed on LL band only. The Centre Area Features and Edge Parameters are determined on each DWT level by considering all four sub bands. The comparison of test fingerprint with database fingerprint is decided based on the Euclidean Distance of all the features. It is observed that the values of FAR, FRR and TSR are improved compared to the existing algorithm. | DWT Based Fingerprint Recognition using Non Minutiae Features | 3,331 |
Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods. | Face Identification from Manipulated Facial Images using SIFT | 3,332 |
A line of a bilingual document page may contain text words in regional language and numerals in English. For Optical Character Recognition (OCR) of such a document page, it is necessary to identify different script forms before running an individual OCR system. In this paper, we have identified a tool of morphological opening by reconstruction of an image in different directions and regional descriptors for script identification at word level, based on the observation that every text has a distinct visual appearance. The proposed system is developed for three Indian major bilingual documents, Kannada, Telugu and Devnagari containing English numerals. The nearest neighbour and k-nearest neighbour algorithms are applied to classify new word images. The proposed algorithm is tested on 2625 words with various font styles and sizes. The results obtained are quite encouraging | Morphological Reconstruction for Word Level Script Identification | 3,333 |
Although radiologists can employ CAD systems to characterize malignancies, pulmonary fibrosis and other chronic diseases; the design of imaging techniques to quantify infectious diseases continue to lag behind. There exists a need to create more CAD systems capable of detecting and quantifying characteristic patterns often seen in respiratory tract infections such as influenza, bacterial pneumonia, or tuborculosis. One of such patterns is Tree-in-bud (TIB) which presents \textit{thickened} bronchial structures surrounding by clusters of \textit{micro-nodules}. Automatic detection of TIB patterns is a challenging task because of their weak boundary, noisy appearance, and small lesion size. In this paper, we present two novel methods for automatically detecting TIB patterns: (1) a fast localization of candidate patterns using information from local scale of the images, and (2) a M\"{o}bius invariant feature extraction method based on learned local shape and texture properties. A comparative evaluation of the proposed methods is presented with a dataset of 39 laboratory confirmed viral bronchiolitis human parainfluenza (HPIV) CTs and 21 normal lung CTs. Experimental results demonstrate that the proposed CAD system can achieve high detection rate with an overall accuracy of 90.96%. | Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities
for CAD Systems | 3,334 |
We present a fully automated method for top-down segmentation of the pulmonary arterial tree in low-dose thoracic CT images. The main basal pulmonary arteries are identified near the lung hilum by searching for candidate vessels adjacent to known airways, identified by our previously reported airway segmentation method. Model cylinders are iteratively fit to the vessels to track them into the lungs. Vessel bifurcations are detected by measuring the rate of change of vessel radii, and child vessels are segmented by initiating new trackers at bifurcation points. Validation is accomplished using our novel sparse surface (SS) evaluation metric. The SS metric was designed to quantify the magnitude of the segmentation error per vessel while significantly decreasing the manual marking burden for the human user. A total of 210 arteries and 205 veins were manually marked across seven test cases. 134/210 arteries were correctly segmented, with a specificity for arteries of 90%, and average segmentation error of 0.15 mm. This fully-automated segmentation is a promising method for improving lung nodule detection in low-dose CT screening scans, by separating vessels from surrounding iso-intensity objects. | Automated segmentation of the pulmonary arteries in low-dose CT by
vessel tracking | 3,335 |
Augmented reality has became an useful tool in many areas from space exploration to military applications. Although used theoretical principles are well known for almost a decade, the augmented reality is almost exclusively used in high budget solutions with a special hardware. However, in last few years we could see rising popularity of many projects focused on deployment of the augmented reality on different mobile devices. Our article is aimed on developers who consider development of an augmented reality application for the mainstream market. Such developers will be forced to keep the application price, therefore also the development price, at reasonable level. Usage of existing image processing software library could bring a significant cut-down of the development costs. In the theoretical part of the article is presented an overview of the augmented reality application structure. Further, an approach for selection appropriate library as well as the review of the existing software libraries focused in this area is described. The last part of the article outlines our implementation of key parts of the augmented reality application using the OpenCV library. | Augmented Reality Implementation Methods in Mainstream Applications | 3,336 |
Augmented reality have undergone considerable improvement in past years. Many special techniques and hardware devices were developed, but the crucial breakthrough came with the spread of intelligent mobile phones. This enabled mass spread of augmented reality applications. However mobile devices have limited hardware capabilities, which narrows down the methods usable for scene analysis. In this article we propose an augmented reality application which is using cloud computing to enable using of more complex computational methods such as neural networks. Our goal is to create an affordable augmented reality application suitable which will help car designers in by 'virtualizing' car modifications. | Mobile Augmented Reality Applications | 3,337 |
The performance of the Fingerprint recognition system will be more accurate with respect of enhancement for the fingerprint images. In this paper we develop a novel method for Fingerprint image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition (SVD) has been proposed. This technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization. An automatic histogram threshold approach based on a fuzziness measure is presented. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between ridges and valleys of the best, medium and poor finger image features to extract from finger images and get maximum recognition rate using fuzzy measures. The experimental results show the recognition of superiority of the proposed method to get maximum performance up gradation to the implementation of this approach. | Fingerprint: DWT, SVD Based Enhancement and Significant Contrast for
Ridges and Valleys Using Fuzzy Measures | 3,338 |
In this paper we propose a measure of anisotropy as a quality parameter to estimate the amount of noise in noisy images. The anisotropy of an image can be determined through a directional measure, using an appropriate statistical distribution of the information contained in the image. This new measure is achieved through a stack filtering paradigm. First, we define a local directional entropy, based on the distribution of 0's and 1's in the neigborhood of every pixel location of each stack level. Then the entropy variation of this directional entropy is used to define an anisotropic measure. The empirical results have shown that this measure can be regarded as an excellent image noise indicator, which is particularly relevant for quality assessment of denoising algorithms. The method has been evaluated with artificial and real-world degraded images. | Image denoising assessment using anisotropic stack filtering | 3,339 |
Using a vehicle toy (in next future called vehicle) as a moving object an automatic road lighting system (ARLS) model is constructed. A digital video camera with 25 fps is used to capture the vehicle motion as it moves in the test segment of the road. Captured images are then processed to calculate vehicle speed. This information of the speed together with position of vehicle is then used to control the lighting system along the path that passes by the vehicle. Length of the road test segment is 1 m, the video camera is positioned about 1.1 m above the test segment, and the vehicle toy dimension is 13 cm \times 9.3 cm. In this model, the maximum speed that ARLS can handle is about 1.32 m/s, and the highest performance is obtained about 91% at speed 0.93 m/s. | Automatic Road Lighting System (ARLS) Model Based on Image Processing of
Moving Object | 3,340 |
We propose a traffic congestion estimation system based on unsupervised on-line learning algorithm. The system does not rely on background extraction or motion detection. It extracts local features inside detection regions of variable size which are drawn on lanes in advance. The extracted features are then clustered into two classes using K-means and Gaussian Mixture Models(GMM). A Bayes classifier is used to detect vehicles according to the previous cluster information which keeps updated whenever system is running by on-line EM algorithm. Experimental result shows that our system can be adapted to various traffic scenes for estimating traffic status. | Online Vehicle Detection For Estimating Traffic Status | 3,341 |
This paper presents multi-font/multi-size Kannada numerals and vowels recognition based on spatial features. Directional spatial features viz stroke density, stroke length and the number of stokes in an image are employed as potential features to characterize the printed Kannada numerals and vowels. Based on these features 1100 numerals and 1400 vowels are classified with Multi-class Support Vector Machines (SVM). The proposed system achieves the recognition accuracy as 98.45% and 90.64% for numerals and vowels respectively. | Spatial Features for Multi-Font/Multi-Size Kannada Numerals and Vowels
Recognition | 3,342 |
We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the minimum value of the optimization model of LRR is equal to the rank of the data matrix. For the noisy case, we show that LRR can be approximated as a factorization method that combines noise removal by column sparse robust PCA. We further propose an improved version of LRR, called Robust Shape Interaction (RSI), which uses the corrected data as the dictionary instead of the noisy data. RSI is more robust than LRR when the corruption in data is heavy. Experiments on both synthetic and real data testify to the improved robustness of RSI. | Analysis and Improvement of Low Rank Representation for Subspace
segmentation | 3,343 |
This paper reviews Kunchenko's polynomials using as template matching method to recognize template in one-dimensional input signal. Kunchenko's polynomials method is compared with classical methods - cross-correlation and sum of squared differences according to numerical statistical example. | Kunchenko's Polynomials for Template Matching | 3,344 |
The box counting method for fractal dimension estimation had not been applied to large or colour images thus far due to the processing time required. In this letter we present a fast, easy to implement and very easily expandable to any number of dimensions variation, the box merging method. It is applied here in RGB images which are considered as sets in 5-D space. | A Variation of the Box-Counting Algorithm Applied to Colour Images | 3,345 |
This study poses the feature correspondence problem as a hypergraph node labeling problem. Candidate feature matches and their subsets (usually of size larger than two) are considered to be the nodes and hyperedges of a hypergraph. A hypergraph labeling algorithm, which models the subset-wise interaction by an undirected graphical model, is applied to label the nodes (feature correspondences) as correct or incorrect. We describe a method to learn the cost function of this labeling algorithm from labeled examples using a graphical model training algorithm. The proposed feature matching algorithm is different from the most of the existing learning point matching methods in terms of the form of the objective function, the cost function to be learned and the optimization method applied to minimize it. The results on standard datasets demonstrate how learning over a hypergraph improves the matching performance over existing algorithms, notably one that also uses higher order information without learning. | Learning Hypergraph Labeling for Feature Matching | 3,346 |
This paper shows that the k-means quantization of a signal can be interpreted both as a crisp indicator function and as a fuzzy membership assignment describing fuzzy clusters and fuzzy boundaries. Combined crisp and fuzzy indicator functions are defined here as natural generalizations of the ordinary crisp and fuzzy indicator functions, respectively. An application to iris segmentation is presented together with a demo program. | A Fuzzy View on k-Means Based Signal Quantization with Application in
Iris Segmentation | 3,347 |
A new approach in iris recognition based on Circular Fuzzy Iris Segmentation (CFIS) and Gabor Analytic Iris Texture Binary Encoder (GAITBE) is proposed and tested here. CFIS procedure is designed to guarantee that similar iris segments will be obtained for similar eye images, despite the fact that the degree of occlusion may vary from one image to another. Its result is a circular iris ring (concentric with the pupil) which approximates the actual iris. GAITBE proves better encoding of statistical independence between the iris codes extracted from different irides using Hilbert Transform. Irides from University of Bath Iris Database are binary encoded on two different lengths (768 / 192 bytes) and tested in both single-enrollment and multi-enrollment identification scenarios. All cases illustrate the capacity of the newly proposed methodology to narrow down the distribution of inter-class matching scores, and consequently, to guarantee a steeper descent of the False Accept Rate. | Exploring New Directions in Iris Recognition | 3,348 |
Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shape-based graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach. | Topographic Feature Extraction for Bengali and Hindi Character Images | 3,349 |
Face recognition has been studied extensively for more than 20 years now. Since the beginning of 90s the subject has became a major issue. This technology is used in many important real-world applications, such as video surveillance, smart cards, database security, internet and intranet access. This report reviews recent two algorithms for face recognition which take advantage of a relatively new multiscale geometric analysis tool - Curvelet transform, for facial processing and feature extraction. This transform proves to be efficient especially due to its good ability to detect curves and lines, which characterize the human's face. An algorithm which is based on the two algorithms mentioned above is proposed, and its performance is evaluated on three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech. k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are used, along with Principal Component Analysis (PCA) for dimensionality reduction. This algorithm shows good results, and it even outperforms other algorithms in some cases. | Face Recognition using Curvelet Transform | 3,350 |
Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models...are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we use the final fingerprint to match with others in FVC2004 fingerprint database (DB4) to show the capability of the model. | Fingerprint recognition using standardized fingerprint model | 3,351 |
Facial expression classification is a kind of image classification and it has received much attention, in recent years. There are many approaches to solve these problems with aiming to increase efficient classification. One of famous suggestions is described as first step, project image to different spaces; second step, in each of these spaces, images are classified into responsive class and the last step, combine the above classified results into the final result. The advantages of this approach are to reflect fulfill and multiform of image classified. In this paper, we use 2D-PCA and its variants to project the pattern or image into different spaces with different grouping strategies. Then we develop a model which combines many Neural Networks applied for the last step. This model evaluates the reliability of each space and gives the final classification conclusion. Our model links many Neural Networks together, so we call it Multi Artificial Neural Network (MANN). We apply our proposal model for 6 basic facial expressions on JAFFE database consisting 213 images posed by 10 Japanese female models. | Facial Expression Classification Based on Multi Artificial Neural
Network and Two Dimensional Principal Component Analysis | 3,352 |
In remote sensing, image fusion technique is a useful tool used to fuse high spatial resolution panchromatic images (PAN) with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion (F) while preserving the spectral information in the multispectral image (MS).There are many PAN sharpening techniques or Pixel-Based image fusion techniques that have been developed to try to enhance the spatial resolution and the spectral property preservation of the MS. This paper attempts to undertake the study of image fusion, by using two types of pixel-based image fusion techniques i.e. Arithmetic Combination and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques. The first type includes Brovey Transform (BT), Color Normalized Transformation (CN) and Multiplicative Method (MLT). The second type include High-Pass Filter Additive Method (HPFA), High-Frequency-Addition Method (HFA) High Frequency Modulation Method (HFM) and The Wavelet transform-based fusion method (WT). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), Entropy(En), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively. | Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion
Techniques | 3,353 |
The IHS sharpening technique is one of the most commonly used techniques for sharpening. Different transformations have been developed to transfer a color image from the RGB space to the IHS space. Through literature, it appears that, various scientists proposed alternative IHS transformations and many papers have reported good results whereas others show bad ones as will as not those obtained which the formula of IHS transformation were used. In addition to that, many papers show different formulas of transformation matrix such as IHS transformation. This leads to confusion what is the exact formula of the IHS transformation?. Therefore, the main purpose of this work is to explore different IHS transformation techniques and experiment it as IHS based image fusion. The image fusion performance was evaluated, in this study, using various methods to estimate the quality and degree of information improvement of a fused image quantitatively. | The IHS Transformations Based Image Fusion | 3,354 |
This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2 to 4 dB. | Correlation Estimation from Compressed Images | 3,355 |
In this paper, we propose a novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by orientation distribution functions (ODFs). Our proposed algorithm seeks an optimal diffeomorphism of large deformation between two ODF fields in a spatial volume domain and at the same time, locally reorients an ODF in a manner such that it remains consistent with the surrounding anatomical structure. To this end, we first review the Riemannian manifold of ODFs. We then define the reorientation of an ODF when an affine transformation is applied and subsequently, define the diffeomorphic group action to be applied on the ODF based on this reorientation. We incorporate the Riemannian metric of ODFs for quantifying the similarity of two HARDI images into a variational problem defined under the large deformation diffeomorphic metric mapping (LDDMM) framework. We finally derive the gradient of the cost function in both Riemannian spaces of diffeomorphisms and the ODFs, and present its numerical implementation. Both synthetic and real brain HARDI data are used to illustrate the performance of our registration algorithm. | Diffeomorphic Metric Mapping of High Angular Resolution Diffusion
Imaging based on Riemannian Structure of Orientation Distribution Functions | 3,356 |
This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns. We investigate the error function used for kernel approximation and its relation to the properties of the input signal. Based on natural image statistics we propose a quadratic form kernel error function so that the output image l2 error is minimized. We apply the proposed approach to approximate the Gaussian kernel by linear combination of constant functions. This results in very efficient Gaussian filtering method. Our experiments show that the proposed technique is faster than state of the art methods while preserving a similar accuracy. | Efficient and Accurate Gaussian Image Filtering Using Running Sums | 3,357 |
We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated weight image to track objects. Recent improvements on the original mean-shift algorithm involves using a classifier that differentiates the object from its surroundings. We adopt this classifier-based approach and propose an application of a classifier fusion technique within this classifier-based context in this work. We use two different classifiers, where one comes from a background modeling method, to generate the weight image and we calculate contributions of the classifiers dynamically using their confidences to generate a final weight image to be used in tracking. The contributions of the classifiers are calculated by using correlations between histograms of their weight images and histogram of a defined ideal weight image in the previous frame. We show with experiments that our dynamic combination scheme selects good contributions for classifiers for different cases and improves tracking accuracy significantly. | Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking | 3,358 |
We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obtained recall rate almost doubles the best reported result to date. We discuss the various components and innovations of our system that enable this significant performance gap. These components include extensive utilization of an accurate 3D reconstructed shape model dealing with challenges arising from pose and illumination. In addition, discriminative models based on billions of faces are used in order to overcome aging and facial expression as well as low light and overexposure. Finally, we identify a challenging set of identification queries that might provide useful focus for future research. | Leveraging Billions of Faces to Overcome Performance Barriers in
Unconstrained Face Recognition | 3,359 |
We give an algorithm that learns a representation of data through compression. The algorithm 1) predicts bits sequentially from those previously seen and 2) has a structure and a number of computations similar to an autoencoder. The likelihood under the model can be calculated exactly, and arithmetic coding can be used directly for compression. When training on digits the algorithm learns filters similar to those of restricted boltzman machines and denoising autoencoders. Independent samples can be drawn from the model by a single sweep through the pixels. The algorithm has a good compression performance when compared to other methods that work under random ordering of pixels. | Learning Representations by Maximizing Compression | 3,360 |
This paper presents an automated system for human face recognition in a real time background world for a large homemade dataset of persons face. The task is very difficult as the real time background subtraction in an image is still a challenge. Addition to this there is a huge variation in human face image in terms of size, pose and expression. The system proposed collapses most of this variance. To detect real time human face AdaBoost with Haar cascade is used and a simple fast PCA and LDA is used to recognize the faces detected. The matched face is then used to mark attendance in the laboratory, in our case. This biometric system is a real time attendance system based on the human face recognition with a simple and fast algorithms and gaining a high accuracy rate.. | Real time face recognition using adaboost improved fast PCA algorithm | 3,361 |
We propose a novel algorithm for compressive imaging that exploits both the sparsity and persistence across scales found in the 2D wavelet transform coefficients of natural images. Like other recent works, we model wavelet structure using a hidden Markov tree (HMT) but, unlike other works, ours is based on loopy belief propagation (LBP). For LBP, we adopt a recently proposed "turbo" message passing schedule that alternates between exploitation of HMT structure and exploitation of compressive-measurement structure. For the latter, we leverage Donoho, Maleki, and Montanari's recently proposed approximate message passing (AMP) algorithm. Experiments with a large image database suggest that, relative to existing schemes, our turbo LBP approach yields state-of-the-art reconstruction performance with substantial reduction in complexity. | Compressive Imaging using Approximate Message Passing and a Markov-Tree
Prior | 3,362 |
There are many image fusion methods that can be used to produce high-resolution mutlispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) of remote sensed images. This paper attempts to undertake the study of image fusion techniques with different Statistical techniques for image fusion as Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Regression variable substitution (RVS), Local Correlation Modeling (LCM) and they are compared with one another so as to choose the best technique, that can be applied on multi-resolution satellite images. This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), Entropy(En), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively. | The Statistical methods of Pixel-Based Image Fusion Techniques | 3,363 |
Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only observations with a zero-mean additive Gaussian noise. The developed algorithm enables the optimal solution for the object wave field reconstruction. Our goal is an improvement of the reconstruction quality with respect to the conventional algorithms. Sparse regularization results in advanced reconstruction accuracy, and numerical simulations demonstrate significant enhancement of imaging. | Advanced phase retrieval: maximum likelihood technique with sparse
regularization of phase and amplitude | 3,364 |
Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as Active Shape and Active Appearance models lack the necessary flexibility for this task, while recent approaches such as the Recursive Compositional Models make model simplifications in order to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer, which is a deformation of a hidden PCA shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state of the art parsing errors on two standard datasets without using any intensity information. | Hierarchical Object Parsing from Structured Noisy Point Clouds | 3,365 |
Until now, of highest relevance for remote sensing data processing and analysis have been techniques for pixel level image fusion. So, This paper attempts to undertake the study of Feature-Level based image fusion. For this purpose, feature based fusion techniques, which are usually based on empirical or heuristic rules, are employed. Hence, in this paper we consider feature extraction (FE) for fusion. It aims at finding a transformation of the original space that would produce such new features, which preserve or improve as much as possible. This study introduces three different types of Image fusion techniques including Principal Component Analysis based Feature Fusion (PCA), Segment Fusion (SF) and Edge fusion (EF). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including (SD), (En), (CC), (SNR), (NRMSE) and (DI) to estimate the quality and degree of information improvement of a fused image quantitatively. | Multisensor Images Fusion Based on Feature-Level | 3,366 |
Detecting the edges of objects within images is critical for quality image processing. We present an edge-detecting technique that uses morphological amoebas that adjust their shape based on variation in image contours. We evaluate the method both quantitatively and qualitatively for edge detection of images, and compare it to classic morphological methods. Our amoeba-based edge-detection system performed better than the classic edge detectors. | Edge detection based on morphological amoebas | 3,367 |
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions. | Biometric Authorization System using Gait Biometry | 3,368 |
In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm. | An Efficient Codebook Initialization Approach for LBG Algorithm | 3,369 |
Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist. | Automatic Application Level Set Approach in Detection Calcifications in
Mammographic Image | 3,370 |
Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios. | Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank
Representation | 3,371 |
Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: \emph{learning PDEs from real data via data-based optimal control}. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way. | Toward Designing Intelligent PDEs for Computer Vision: An Optimal
Control Approach | 3,372 |
The research work presented in this paper is to achieve the tissue classification and automatically diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet based statistical texture analysis method. Comparative studies of texture analysis method are performed for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm (GA) is used to select the optimal texture features from the set of extracted texture features. We construct the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by comparing the classification results of the SVM based classifier with the Back Propagation Neural network classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system has been tested with a number of real Computed Tomography brain images and has achieved satisfactory results. | Automatic Diagnosis of Abnormal Tumor Region from Brain Computed
Tomography Images Using Wavelet Based Statistical Texture Features | 3,373 |
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation. | An Automatic Clustering Technique for Optimal Clusters | 3,374 |
Document segmentation is one of the critical phases in machine recognition of any language. Correct segmentation of individual symbols decides the accuracy of character recognition technique. It is used to decompose image of a sequence of characters into sub images of individual symbols by segmenting lines and words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents consist of vowels, consonants and various modifiers. Hence proper segmentation of Devnagari word is challenging. A simple histogram based approach to segment Devnagari documents is proposed in this paper. Various challenges in segmentation of Devnagari script are also discussed. | Devnagari document segmentation using histogram approach | 3,375 |
Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher order image priors encode high level structural dependencies between pixels and are key to overcoming these problems. However, these priors in general lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher order priors which allow efficient inference. We propose a framework for solving this problem which uses a recently proposed representation of higher order functions where they are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables, which can be done approximately using standard methods. Although this is a primarily theoretical paper, we also demonstrate the practical effectiveness of our framework on the problem of learning a shape prior for image segmentation and reconstruction. We show that our framework can learn a compact representation that approximates a prior that encourages low curvature shapes. We evaluate the approximation accuracy, discuss properties of the trained model, and show various results for shape inpainting and image segmentation. | Curvature Prior for MRF-based Segmentation and Shape Inpainting | 3,376 |
Computational photography involves sophisticated capture methods. A new trend is to capture projection of higher dimensional visual signals such as videos, multi-spectral data and lightfields on lower dimensional sensors. Carefully designed capture methods exploit the sparsity of the underlying signal in a transformed domain to reduce the number of measurements and use an appropriate reconstruction method. Traditional progressive methods may capture successively more detail using a sequence of simple projection basis, such as DCT or wavelets and employ straightforward backprojection for reconstruction. Randomized projection methods do not use any specific sequence and use L0 minimization for reconstruction. In this paper, we analyze the statistical properties of natural images, videos, multi-spectral data and light-fields and compare the effectiveness of progressive and random projections. We define effectiveness by plotting reconstruction SNR against compression factor. The key idea is a procedure to measure best-case effectiveness that is fast, independent of specific hardware and independent of the reconstruction procedure. We believe this is the first empirical study to compare different lossy capture strategies without the complication of hardware or reconstruction ambiguity. The scope is limited to linear non-adaptive sensing. The results show that random projections produce significant advantages over other projections only for higher dimensional signals, and suggest more research to nascent adaptive and non-linear projection methods. | Progressive versus Random Projections for Compressive Capture of Images,
Lightfields and Higher Dimensional Visual Signals | 3,377 |
We present an approach for the joint segmentation and grouping of similar components in anisotropic 3D image data and use it to segment neural tissue in serial sections electron microscopy (EM) images. We first construct a nested set of neuron segmentation hypotheses for each slice. A conditional random field (CRF) then allows us to evaluate both the compatibility of a specific segmentation and a specific inter-slice assignment of neuron candidates with the underlying observations. The model is solved optimally for an entire image stack simultaneously using integer linear programming (ILP), which yields the maximum a posteriori solution in amortized linear time in the number of slices. We evaluate the performance of our approach on an annotated sample of the Drosophila larva neuropil and show that the consideration of different segmentation hypotheses in each slice leads to a significant improvement in the segmentation and assignment accuracy. | Multi-Hypothesis CRF-Segmentation of Neural Tissue in Anisotropic EM
Volumes | 3,378 |
We give a non-iterative solution to a particular case of the four-point three-views pose problem when three camera centers are collinear. Using the well-known Cayley representation of orthogonal matrices, we derive from the epipolar constraints a system of three polynomial equations in three variables. The eliminant of that system is a multiple of a 36th degree univariate polynomial. The true (unique) solution to the problem can be expressed in terms of one of real roots of that polynomial. Experiments on synthetic data confirm that our method is robust enough even in case of planar configurations. | A Non-Iterative Solution to the Four-Point Three-Views Pose Problem in
Case of Collinear Cameras | 3,379 |
This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices. | Design of an Optical Character Recognition System for Camera-based
Handheld Devices | 3,380 |
Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. In opposition, the so-called local methods enforce connectivity but lack robustness to deal with challenging situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. In this paper, we address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional content agree with potential line segments. As a result, our method, which we call STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in several situations where current methods fail. | Connectivity-Enforcing Hough Transform for the Robust Extraction of Line
Segments | 3,381 |
Matlab version 7.1 had been used to detect playing cards on a Casino table and the suits and ranks of these cards had been identified. The process gives an example of an application of computer vision to a problem where rectangular objects are to be detected and the information content of the objects are extracted out. In the case of playing cards, it is the suit and rank of each card. The image processing system is done in two passes. Pass 1 detects rectangular shapes and template matched with a template of the left and right edges of the cards. Pass 2 extracts the suit and rank of the cards by matching the top left portion of the card that contains both rank and suit information, with stored templates of ranks and suits of the playing cards using a series of if-then statements. | Generalised Object Detection and Semantic Analysis: Casino Example using
Matlab | 3,382 |
In the current study, we explore digital homology and cohomology modules, and investigate their fundamental properties on pointed digital images. We also examine pointed digital Hopf spaces and base point preserving digital Hopf functions between the pointed digital Hopf spaces with suitable digital multiplications, and explore the digital primitive homology and cohomology classes, the digital Pontryagin algebras and coalgebras on the digital Hopf spaces as digital images. | Digital (co)homology modules and digital Pontryagin algebras | 3,383 |
We describe an approach for segmenting an image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional, that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach. | Detachable Object Detection: Segmentation and Depth Ordering From
Short-Baseline Video | 3,384 |
This contribution proposes a new approach towards developing a class of probabilistic methods for classifying attributed graphs. The key concept is random attributed graph, which is defined as an attributed graph whose nodes and edges are annotated by random variables. Every node/edge has two random processes associated with it- occurence probability and the probability distribution over the attribute values. These are estimated within the maximum likelihood framework. The likelihood of a random attributed graph to generate an outcome graph is used as a feature for classification. The proposed approach is fast and robust to noise. | Probabilistic prototype models for attributed graphs | 3,385 |
Motivated by an emerging theory of robust low-rank matrix representation, in this paper, we introduce a novel solution for online rigid-body motion registration. The goal is to develop algorithmic techniques that enable a robust, real-time motion registration solution suitable for low-cost, portable 3-D camera devices. Assuming 3-D image features are tracked via a standard tracker, the algorithm first utilizes Robust PCA to initialize a low-rank shape representation of the rigid body. Robust PCA finds the global optimal solution of the initialization, while its complexity is comparable to singular value decomposition. In the online update stage, we propose a more efficient algorithm for sparse subspace projection to sequentially project new feature observations onto the shape subspace. The lightweight update stage guarantees the real-time performance of the solution while maintaining good registration even when the image sequence is contaminated by noise, gross data corruption, outlying features, and missing data. The state-of-the-art accuracy of the solution is validated through extensive simulation and a real-world experiment, while the system enjoys one to two orders of magnitude speed-up compared to well-established RANSAC solutions. The new algorithm will be released online to aid peer evaluation. | Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D
Rigid-Body Motion Registration | 3,386 |
It is well-known that box filters can be efficiently computed using pre-integrations and local finite-differences [Crow1984,Heckbert1986,Viola2001]. By generalizing this idea and by combining it with a non-standard variant of the Central Limit Theorem, a constant-time or O(1) algorithm was proposed in [Chaudhury2010] that allowed one to perform space-variant filtering using Gaussian-like kernels. The algorithm was based on the observation that both isotropic and anisotropic Gaussians could be approximated using certain bivariate splines called box splines. The attractive feature of the algorithm was that it allowed one to continuously control the shape and size (covariance) of the filter, and that it had a fixed computational cost per pixel, irrespective of the size of the filter. The algorithm, however, offered a limited control on the covariance and accuracy of the Gaussian approximation. In this work, we propose some improvements by appropriately modifying the algorithm in [Chaudhury2010]. | Improvements on "Fast space-variant elliptical filtering using box
splines" | 3,387 |
We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from given multiple low-resolution images. An MRF model with the line process supplies a preferable prior for natural images with edges. We improve the existing image transformation model, the compound MRF model, and its hyperparameter prior model. We also derive the optimal estimator -- not the joint maximum a posteriori (MAP) or marginalized maximum likelihood (ML), but the posterior mean (PM) -- from the objective function of the L2-norm (mean square error) -based peak signal-to-noise ratio (PSNR). Point estimates such as MAP and ML are generally not stable in ill-posed high-dimensional problems because of overfitting, while PM is a stable estimator because all the parameters in the model are evaluated as distributions. The estimator is numerically determined by using variational Bayes. Variational Bayes is a widely used method that approximately determines a complicated posterior distribution, but it is generally hard to use because it needs the conjugate prior. We solve this problem with simple Taylor approximations. Experimental results have shown that the proposed method is more accurate or comparable to existing methods. | Posterior Mean Super-resolution with a Causal Gaussian Markov Random
Field Prior | 3,388 |
This paper describes an intelligent system ABHIVYAKTI, which would be pervasive in nature and based on the Computer Vision. It would be very easy in use and deployment. Elder and sick people who are not able to talk or walk, they are dependent on other human beings and need continuous monitoring, while our system provides flexibility to the sick or elder person to announce his or her need to their caretaker by just showing a particular gesture with the developed system, if the caretaker is not nearby. This system will use fingertip detection techniques for acquiring gesture and Artificial Neural Networks (ANNs) will be used for gesture recognition. | ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons | 3,389 |
This article describes a comprehensive system for surveillance and monitoring applications. The development of an efficient real time video motion detection system is motivated by their potential for deployment in the areas where security is the main concern. The paper presents a platform for real time video motion detection and subsequent generation of an alarm condition as set by the parameters of the control system. The prototype consists of a mobile platform mounted with RF camera which provides continuous feedback of the environment. The received visual information is then analyzed by user for appropriate control action, thus enabling the user to operate the system from a remote location. The system is also equipped with the ability to process the image of an object and generate control signals which are automatically transmitted to the mobile platform to track the object. | A Novel comprehensive method for real time Video Motion Detection
Surveillance | 3,390 |
Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t the "leave-one-out margins". The obtained model is termed Optimal Representation Ensemble (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in test faces, a robust version of BPR is proposed by taking the non-face category into consideration. Equipped with the Robust-BPRs, the inference ability of ORE is increased dramatically and several record-breaking accuracies (99.9% on Yale-B and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab) are achieved. It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises. Furthermore, to accommodate immense BPRs sets, a boosting-like algorithm is also derived. The boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype. Besides the empirical superiorities, two desirable features of the proposed methods, namely, the training-determined model-selection and the data-weight-free boosting procedure, are also theoretically verified. | Face Recognition using Optimal Representation Ensemble | 3,391 |
In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discriminability of one classification task (a "distractor" task), while simultaneously preserving information relevant to another (the task-of-interest): For example, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Another example is inter-dataset generalization: when training on a dataset with a particular covariance structure among multiple attributes, it may be useful to suppress one attribute while preserving another so that a trained classifier does not learn spurious correlations between attributes. In this paper we present an algorithm that finds optimal filters to give high discriminability to one task while simultaneously giving low discriminability to a distractor task. We present results showing the effectiveness of the proposed technique on both simulated data and natural face images. | Discriminately Decreasing Discriminability with Learned Image Filters | 3,392 |
Construction of a scale space with a convolution filter has been studied extensively in the past. It has been proven that the only convolution kernel that satisfies the scale space requirements is a Gaussian type. In this paper, we consider a matrix of convolution filters introduced in [1] as a building kernel for a scale space, and shows that we can construct a non-Gaussian scale space with a $2\times 2$ matrix of filters. The paper derives sufficient conditions for the matrix of filters for being a scale space kernel, and present some numerical demonstrations. | Non-Gaussian Scale Space Filtering with 2 by 2 Matrix of Linear Filters | 3,393 |
In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these highinformative horizontal bands precisely, the horizontal band is segmented into several small spatial modules. Dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods. | A Face Recognition Scheme using Wavelet Based Dominant Features | 3,394 |
Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods. | A Comparative Experiment of Several Shape Methods in Recognizing Plants | 3,395 |
This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants. | Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and
Vein Features | 3,396 |
This manuscript describes the elements of a theory of information tailored to control and decision tasks and specifically to visual data. The concept of Actionable Information is described, that relates to a notion of information championed by J. Gibson, and a notion of "complete information" that relates to the minimal sufficient statistics of a complete representation. It is shown that the "actionable information gap" between the two can be reduced by exercising control on the sensing process. Thus, senging, control and information are inextricably tied. This has consequences in the so-called "signal-to-symbol barrier" problem, as well as in the analysis and design of active sensing systems. It has ramifications in vision-based control, navigation, 3-D reconstruction and rendering, as well as detection, localization, recognition and categorization of objects and scenes in live video. This manuscript has been developed from a set of lecture notes for a summer course at the First International Computer Vision Summer School (ICVSS) in Scicli, Italy, in July of 2008. They were later expanded and amended for subsequent lectures in the same School in July 2009. Starting on November 1, 2009, they were further expanded for a special topics course, CS269, taught at UCLA in the Spring term of 2010. | Steps Towards a Theory of Visual Information: Active Perception,
Signal-to-Symbol Conversion and the Interplay Between Sensing and Control | 3,397 |
In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical Car on the Hill control problem. | Closed-Loop Learning of Visual Control Policies | 3,398 |
This paper provides a new algorithm for solving inverse problems, based on the minimization of the $L^2$ norm and on the control of the Total Variation. It consists in relaxing the role of the Total Variation in the classical Total Variation minimization approach, which permits us to get better approximation to the inverse problems. The numerical results on the deconvolution problem show that our method outperforms some previous ones. | Controlled Total Variation regularization for inverse problems | 3,399 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.