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The ubiquity of professional sports and specifically the NFL have lead to an increase in popularity for Fantasy Football. Users have many tools at their disposal: statistics, predictions, rankings of experts and even recommendations of peers. There are issues with all of these, though. Especially since many people pay ... | Fantasy Football Prediction | 2,900 |
Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly show... | Learning with Symmetric Label Noise: The Importance of Being Unhinged | 2,901 |
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the Copeland winner, which, unlike the Condorcet winner, is guaranteed to exist. The first, Copeland Confidence Bound (CCB), is designed for sm... | Copeland Dueling Bandits | 2,902 |
The outputs of a trained neural network contain much richer information than just an one-hot classifier. For example, a neural network might give an image of a dog the probability of one in a million of being a cat but it is still much larger than the probability of being a car. To reveal the hidden structure in them, ... | Unsupervised Learning on Neural Network Outputs: with Application in
Zero-shot Learning | 2,903 |
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of the data, and few studies simultaneously focus on global and local structures as the both of them contain important information. In this pa... | Global and Local Structure Preserving Sparse Subspace Learning: An
Iterative Approach to Unsupervised Feature Selection | 2,904 |
Biclustering involves the simultaneous clustering of objects and their attributes, thus defining local two-way clustering models. Recently, efficient algorithms were conceived to enumerate all biclusters in real-valued datasets. In this case, the solution composes a complete set of maximal and non-redundant biclusters.... | On bicluster aggregation and its benefits for enumerative solutions | 2,905 |
In this paper we propose the Structured Deep Neural Network (Structured DNN) as a structured and deep learning algorithm, learning to find the best structured object (such as a label sequence) given a structured input (such as a vector sequence) by globally considering the mapping relationships between the structure ra... | Towards Structured Deep Neural Network for Automatic Speech Recognition | 2,906 |
Unsupervised feature selection has been always attracting research attention in the communities of machine learning and data mining for decades. In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features. Specifically, our proposed alg... | Unsupervised Feature Analysis with Class Margin Optimization | 2,907 |
In machine learning contests such as the ImageNet Large Scale Visual Recognition Challenge and the KDD Cup, contestants can submit candidate solutions and receive from an oracle (typically the organizers of the competition) the accuracy of their guesses compared to the ground-truth labels. One of the most commonly used... | Exploiting an Oracle that Reports AUC Scores in Machine Learning
Contests | 2,908 |
In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior takes the form of a quadratic function of the labels and permits to encode both attractive and repuls... | Semidefinite and Spectral Relaxations for Multi-Label Classification | 2,909 |
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and impro... | Learning Multiple Tasks with Multilinear Relationship Networks | 2,910 |
In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability ob... | A Recurrent Latent Variable Model for Sequential Data | 2,911 |
We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set. We then propose a boosting method, called QuadBoost, which is strongly supported by the general risk bound and has very simple rules for assigning the voters... | Efficient Learning of Ensembles with QuadBoost | 2,912 |
We consider emphatic temporal-difference learning algorithms for policy evaluation in discounted Markov decision processes with finite spaces. Such algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an improved solution to the problem of divergence of off-policy temporal-difference learning with ... | On Convergence of Emphatic Temporal-Difference Learning | 2,913 |
In this paper, a novel framework of sparse kernel learning for Support Vector Data Description (SVDD) based anomaly detection is presented. In this work, optimal sparse feature selection for anomaly detection is first modeled as a Mixed Integer Programming (MIP) problem. Due to the prohibitively high computational comp... | Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection | 2,914 |
We study the interpretability of conditional probability estimates for binary classification under the agnostic setting or scenario. Under the agnostic setting, conditional probability estimates do not necessarily reflect the true conditional probabilities. Instead, they have a certain calibration property: among all d... | On the Interpretability of Conditional Probability Estimates in the
Agnostic Setting | 2,915 |
The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Prin... | Max-Entropy Feed-Forward Clustering Neural Network | 2,916 |
Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architec... | Margin-Based Feed-Forward Neural Network Classifiers | 2,917 |
In this document, we show that the algorithm CoCoA+ (Ma et al., ICML, 2015) under the setting used in their experiments, which is also the best setting suggested by the authors that proposed this algorithm, is equivalent to the practical variant of DisDCA (Yang, NIPS, 2013). | On the Equivalence of CoCoA+ and DisDCA | 2,918 |
This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent ... | Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to
Novel Algorithms | 2,919 |
We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from t... | Localized Multiple Kernel Learning---A Convex Approach | 2,920 |
This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in a single-pass by improving its model after analyzing each data point and discarding it thereafter. Nevertheless, it suffers from the scalabi... | A Fast Incremental Gaussian Mixture Model | 2,921 |
The online learning of deep neural networks is an interesting problem of machine learning because, for example, major IT companies want to manage the information of the massive data uploaded on the web daily, and this technology can contribute to the next generation of lifelong learning. We aim to train deep models fro... | Dual Memory Architectures for Fast Deep Learning of Stream Data via an
Online-Incremental-Transfer Strategy | 2,922 |
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a mini-batch of data samples to estimate the gradient of log-posterior and fur... | Learning Deep Generative Models with Doubly Stochastic MCMC | 2,923 |
Deep directed generative models have attracted much attention recently due to their expressive representation power and the ability of ancestral sampling. One major difficulty of learning directed models with many latent variables is the intractable inference. To address this problem, most existing algorithms make assu... | Latent Regression Bayesian Network for Data Representation | 2,924 |
We consider stochastic sequential learning problems where the learner can observe the \textit{average reward of several actions}. Such a setting is interesting in many applications involving monitoring and surveillance, where the set of the actions to observe represent some (geographical) area. The importance of this s... | Cheap Bandits | 2,925 |
We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong... | Online Gradient Boosting | 2,926 |
We study supervised learning problems using clustering constraints to impose structure on either features or samples, seeking to help both prediction and interpretation. The problem of clustering features arises naturally in text classification for instance, to reduce dimensionality by grouping words together and ident... | Learning with Clustering Structure | 2,927 |
Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability distribution is defined by the... | Numeric Input Relations for Relational Learning with Applications to
Community Structure Analysis | 2,928 |
People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Radema... | On the Depth of Deep Neural Networks: A Theoretical View | 2,929 |
In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world. Estimating the gradient of this loss function, using samples, lies at th... | Gradient Estimation Using Stochastic Computation Graphs | 2,930 |
We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure... | Scalable Semi-Supervised Aggregation of Classifiers | 2,931 |
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function --- ... | The Extreme Value Machine | 2,932 |
Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individ... | Strategic Classification | 2,933 |
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago where the proposed approaches to combat the noise revolve around a Per-ceptron learning scheme fed with peculiar examples computed through a weighted averag... | Unconfused ultraconservative multiclass algorithms | 2,934 |
The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizin... | Flexible Multi-layer Sparse Approximations of Matrices and Applications | 2,935 |
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the progr... | Splash: User-friendly Programming Interface for Parallelizing Stochastic
Algorithms | 2,936 |
We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnormalized log-probability of the data. We show that the conditions for conservativeness are more general than for encoder and decoder weights to... | Conservativeness of untied auto-encoders | 2,937 |
We present a complimentary objective for training recurrent neural networks (RNN) with gating units that helps with regularization and interpretability of the trained model. Attention-based RNN models have shown success in many difficult sequence to sequence classification problems with long and short term dependencies... | Occam's Gates | 2,938 |
This paper presents a multiscale decomposition algorithm. Unlike standard wavelet transforms, the proposed operator is both linear and shift invariant. The central idea is to obtain shift invariance by averaging the aligned wavelet transform projections over all circular shifts of the signal. It is shown how the same t... | A Linear Shift Invariant Multiscale Transform | 2,939 |
We give a systematic, abstract formulation of the image normalization method as applied to a general group of image transformations, and then illustrate the abstract analysis by applying it to the hierarchy of viewing transformations of a planar object. | General Theory of Image Normalization | 2,940 |
This paper presents an invariant under scaling and linear brightness change. The invariant is based on differentials and therefore is a local feature. Rotationally invariant 2-d differential Gaussian operators up to third order are proposed for the implementation of the invariant. The performance is analyzed by simulat... | A Differential Invariant for Zooming | 2,941 |
We describe a simple, but efficient algorithm for the generation of dilated contours from bilevel images. The initial part of the contour extraction is explained to be a good candidate for parallel computer code generation. The remainder of the algorithm is of linear nature. | A Parallel Algorithm for Dilated Contour Extraction from Bilevel Images | 2,942 |
Fractal image compression, Culik's image compression and zerotree prediction coding of wavelet image decomposition coefficients succeed only because typical images being compressed possess a significant degree of self-similarity. Besides the common concept, these methods turn out to be even more tightly related, to the... | Image Compression with Iterated Function Systems, Finite Automata and
Zerotrees: Grand Unification | 2,943 |
This paper presents invariants under gamma correction and similarity transformations. The invariants are local features based on differentials which are implemented using derivatives of the Gaussian. The use of the proposed invariant representation is shown to yield improved correlation results in a template matching s... | Differential Invariants under Gamma Correction | 2,944 |
This work deals with content-based video indexing. Our viewpoint is semi-automatic analysis of compressed video. We consider the possible applications of motion analysis and moving object detection : assisting moving object indexing, summarising videos, and allowing image and motion queries. We propose an approach base... | Assisted Video Sequences Indexing : Motion Analysis Based on Interest
Points | 2,945 |
The paper has established and verified the theory prevailing widely among image and pattern recognition specialists that the bottom-up indirect regional matching process is the more stable and the more robust than the global matching process against concentrated types of noise represented by clutter, outlier or occlusi... | Robustness of Regional Matching Scheme over Global Matching Scheme | 2,946 |
A Bayesian classifier that up-weights the differences in the attribute values is discussed. Using four popular datasets from the UCI repository, some interesting features of the network are illustrated. The network is suitable for classification problems. | Boosting the Differences: A fast Bayesian classifier neural network | 2,947 |
The difference-boosting algorithm is used on letters dataset from the UCI repository to classify distorted raster images of English alphabets. In contrast to rather complex networks, the difference-boosting is found to produce comparable or better classification efficiency on this complex problem. | Distorted English Alphabet Identification : An application of Difference
Boosting Algorithm | 2,948 |
We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to ... | Geometric Morphology of Granular Materials | 2,949 |
The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to b... | Probabilistic Search for Object Segmentation and Recognition | 2,950 |
We study theoretical and computational aspects of the least squares fit (LSF) of circles and circular arcs. First we discuss the existence and uniqueness of LSF and various parametrization schemes. Then we evaluate several popular circle fitting algorithms and propose a new one that surpasses the existing methods in re... | Least squares fitting of circles and lines | 2,951 |
We study the problem of fitting parametrized curves to noisy data. Under certain assumptions (known as Cartesian and radial functional models), we derive asymptotic expressions for the bias and the covariance matrix of the parameter estimates. We also extend Kanatani's version of the Cramer-Rao lower bound, which he pr... | Statistical efficiency of curve fitting algorithms | 2,952 |
Most algorithms in 3D computer vision rely on the pinhole camera model because of its simplicity, whereas virtually all imaging devices introduce certain amount of nonlinear distortion, where the radial distortion is the most severe part. Common approach to radial distortion is by the means of polynomial approximation,... | Flexible Camera Calibration Using a New Analytical Radial Undistortion
Formula with Application to Mobile Robot Localization | 2,953 |
Common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as w... | A New Analytical Radial Distortion Model for Camera Calibration | 2,954 |
The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion ... | Rational Radial Distortion Models with Analytical Undistortion Formulae | 2,955 |
The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion ... | An Analytical Piecewise Radial Distortion Model for Precision Camera
Calibration | 2,956 |
The task of camera calibration is to estimate the intrinsic and extrinsic parameters of a camera model. Though there are some restricted techniques to infer the 3-D information about the scene from uncalibrated cameras, effective camera calibration procedures will open up the possibility of using a wide range of existi... | Camera Calibration: a USU Implementation | 2,957 |
The commonly used radial distortion model for camera calibration is in fact an assumption or a restriction. In practice, camera distortion could happen in a general geometrical manner that is not limited to the radial sense. This paper proposes a simplified geometrical distortion modeling method by using two different ... | A Family of Simplified Geometric Distortion Models for Camera
Calibration | 2,958 |
In the paper will be presented a safety and security system based on fingerprint technology. The results suggest a new scenario where the new cars can use a fingerprint sensor integrated in car handle to allow access and in the dashboard as starter button. | Fingerprint based bio-starter and bio-access | 2,959 |
For many tracking and surveillance applications, background subtraction provides an effective means of segmenting objects moving in front of a static background. Researchers have traditionally used combinations of morphological operations to remove the noise inherent in the background-subtracted result. Such techniques... | Better Foreground Segmentation Through Graph Cuts | 2,960 |
A method of temporal factor prognosis of TE (tick-borne encephalitis) infection has been developed. The high precision of the prognosis results for a number of geographical regions of Primorsky Krai has been achieved. The method can be applied not only to epidemiological research but also to others. | Factor Temporal Prognosis of Tick-Borne Encephalitis Foci Functioning on
the South of Russian Far East | 2,961 |
Despite encouraging recent progresses in ensemble approaches, classification methods seem to have reached a plateau in development. Further advances depend on a better understanding of geometrical and topological characteristics of point sets in high-dimensional spaces, the preservation of such characteristics under fe... | Geometrical Complexity of Classification Problems | 2,962 |
This publication presents methods for face detection, analysis and recognition: fast normalized cross-correlation (fast correlation coefficient) between multiple templates based face pre-detection method, method for detection of exact face contour based on snakes and Generalized Gradient Vector Flow field, method for c... | Computerized Face Detection and Recognition | 2,963 |
This paper presents a blind detection and compensation technique for camera lens geometric distortions. The lens distortion introduces higher-order correlations in the frequency domain and in turn it can be detected using higher-order spectral analysis tools without assuming any specific calibration target. The existin... | Blind Detection and Compensation of Camera Lens Geometric Distortions | 2,964 |
We study image compression by a separable wavelet basis $\big\{\psi(2^{k_1}x-i)\psi(2^{k_2}y-j),$ $\phi(x-i)\psi(2^{k_2}y-j),$ $\psi(2^{k_1}(x-i)\phi(y-j),$ $\phi(x-i)\phi(y-i)\big\},$ where $k_1, k_2 \in \mathbb{Z}_+$; $i,j\in\mathbb{Z}$; and $\phi,\psi$ are elements of a standard biorthogonal wavelet basis in $L_2(\m... | Image compression by rectangular wavelet transform | 2,965 |
The Gradient Vector Flow (GVF) is a vector diffusion approach based on Partial Differential Equations (PDEs). This method has been applied together with snake models for boundary extraction medical images segmentation. The key idea is to use a diffusion-reaction PDE to generate a new external force field that makes sna... | Gradient Vector Flow Models for Boundary Extraction in 2D Images | 2,966 |
Image information content is known to be a complicated and controvercial problem. This paper posits a new image information content definition. Following the theory of Solomonoff-Kolmogorov-Chaitin's complexity, we define image information content as a set of descriptions of imafe data structures. Three levels of such ... | Searching for image information content, its discovery, extraction, and
representation | 2,967 |
In this paper we present an unconventional image segmentation approach which is devised to meet the requirements of image understanding and pattern recognition tasks. Generally image understanding assumes interplay of two sub-processes: image information content discovery and image information content interpretation. D... | Paving the Way for Image Understanding: A New Kind of Image
Decomposition is Desired | 2,968 |
We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local a... | Automatic Face Recognition System Based on Local Fourier-Bessel Features | 2,969 |
A novel biologically motivated face recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Next, the Euclidean distance between all images is computed and each image is now represented by its dissimilarity to the other i... | Face Recognition Based on Polar Frequency Features | 2,970 |
We present a novel local-based face verification system whose components are analogous to those of biological systems. In the proposed system, after global registration and normalization, three eye regions are converted from the spatial to polar frequency domain by a Fourier-Bessel Transform. The resulting representati... | Face Verification in Polar Frequency Domain: a Biologically Motivated
Approach | 2,971 |
We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transfo... | Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised
Classification | 2,972 |
In this paper, a decision support system for ship identification is presented. The system receives as input a silhouette of the vessel to be identified, previously extracted from a side view of the object. This view could have been acquired with imaging sensors operating at different spectral ranges (CCD, FLIR, image i... | A decision support system for ship identification based on the curvature
scale space representation | 2,973 |
Metal melting on release after explosion is a physical system far from quilibrium. A complete physical model of this system does not exist, because many interrelated effects have to be considered. General methodology needs to be developed so as to describe and understand physical phenomena involved. The high noise of t... | Understanding physics from interconnected data | 2,974 |
A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of ... | The Perceptron Algorithm: Image and Signal Decomposition, Compression,
and Analysis by Iterative Gaussian Blurring | 2,975 |
This short article presents an alternative view of high resolution imaging from various sources with the aim of the discovery of potential sites of archaeological importance, or sites that exhibit `anomalies' such that they may merit closer inspection and analysis. It is conjectured, and to a certain extent demonstrate... | The `Face on Mars': a photographic approach for the search of signs of
past civilizations from a macroscopic point of view, factoring long-term
erosion in image reconstruction | 2,976 |
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for t... | Multilevel Thresholding for Image Segmentation through a Fast
Statistical Recursive Algorithm | 2,977 |
We present an algorithm that enables one to perform locally adaptive block thresholding, while maintaining image continuity. Images are divided into sub-images based some standard image attributes and thresholding technique is employed over the sub-images. The present algorithm makes use of the thresholds of neighborin... | Locally Adaptive Block Thresholding Method with Continuity Constraint | 2,978 |
This communication describes a representation of images as a set of edges characterized by their position and orientation. This representation allows the comparison of two images and the computation of their similarity. The first step in this computation of similarity is the seach of a geometrical basis of the two dime... | Matching Edges in Images ; Application to Face Recognition | 2,979 |
In this paper, we focus on Fourier analysis and holographic transforms for signal representation. For instance, in the case of image processing, the holographic representation has the property that an arbitrary portion of the transformed image enables reconstruction of the whole image with details missing. We focus on ... | Fourier Analysis and Holographic Representations of 1D and 2D Signals | 2,980 |
Feature extraction and matching are among central problems of computer vision. It is inefficent to search features over all locations and scales. Neurophysiological evidence shows that to locate objects in a digital image the human visual system employs visual attention to a specific object while ignoring others. The b... | Biologically Inspired Hierarchical Model for Feature Extraction and
Localization | 2,981 |
In this article we propose a novel face recognition method based on Principal Component Analysis (PCA) and Log-Gabor filters. The main advantages of the proposed method are its simple implementation, training, and very high recognition accuracy. For recognition experiments we used 5151 face images of 1311 persons from ... | Face Recognition using Principal Component Analysis and Log-Gabor
Filters | 2,982 |
In this article we propose a method for the recognition of faces with different facial expressions. For recognition we extract feature vectors by using log-Gabor filters of multiple orientations and scales. Using sliding window algorithm and variances -based masking these features are extracted at image regions that ar... | Recognition of expression variant faces using masked log-Gabor features
and Principal Component Analysis | 2,983 |
Regularization functionals that lower level set boundary length when used with L^1 fidelity functionals on signal de-noising on images create artifacts. These are (i) rounding of corners, (ii) shrinking of radii, (iii) shrinking of cusps, and (iv) non-smoothing of staircasing. Regularity functionals based upon total cu... | Notes on Geometric Measure Theory Applications to Image Processing;
De-noising, Segmentation, Pattern, Texture, Lines, Gestalt and Occlusion | 2,984 |
Raster images can have a range of various distortions connected to their raster structure. Upsampling them might in effect substantially yield the raster structure of the original image, known as aliasing. The upsampling itself may introduce aliasing into the upsampled image as well. The presented method attempts to re... | An effective edge--directed frequency filter for removal of aliasing in
upsampled images | 2,985 |
In this paper, we are interested in the application to video segmentation of the discrete shape optimization problem involving the shape weighted perimeter and an additional term depending on a parameter. Based on recent works and in particular the one of Darbon and Sigelle, we justify the equivalence of the shape opti... | Total Variation Minimization and Graph Cuts for Moving Objects
Segmentation | 2,986 |
We present conditional expression (CE) for finding blurs convolved in given images. The CE is given in terms of the zero-values of the blurs evaluated at multi-point. The CE can detect multiple blur all at once. We illustrate the multiple blur-detection by using a test image. | Conditional Expressions for Blind Deconvolution: Multi-point form | 2,987 |
We developed novel conditional expressions (CEs) for Lane and Bates' blind deconvolution. The CEs are given in term of the derivatives of the zero-values of the z-transform of given images. The CEs make it possible to automatically detect multiple blur convolved in the given images all at once without performing any an... | Conditional Expressions for Blind Deconvolution: Derivative form | 2,988 |
In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in... | Camera motion estimation through planar deformation determination | 2,989 |
Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when... | A higher-order active contour model of a `gas of circles' and its
application to tree crown extraction | 2,990 |
Irregular pyramids are made of a stack of successively reduced graphs embedded in the plane. Such pyramids are used within the segmentation framework to encode a hierarchy of partitions. The different graph models used within the irregular pyramid framework encode different types of relationships between regions. This ... | Contains and Inside relationships within combinatorial Pyramids | 2,991 |
The aim of this study is to detect man-made cartographic objects in high-resolution satellite images. New generation satellites offer a sub-metric spatial resolution, in which it is possible (and necessary) to develop methods at object level rather than at pixel level, and to exploit structural features of objects. Wit... | Extraction of cartographic objects in high resolution satellite images
for object model generation | 2,992 |
There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specif... | Text Line Segmentation of Historical Documents: a Survey | 2,993 |
We propose a new algorithm to the problem of polygonal curve approximation based on a multiresolution approach. This algorithm is suboptimal but still maintains some optimality between successive levels of resolution using dynamic programming. We show theoretically and experimentally that this algorithm has a linear co... | Multiresolution Approximation of Polygonal Curves in Linear Complexity | 2,994 |
This paper presents deformable templates as a tool for segmentation and localization of biological structures in medical images. Structures are represented by a prototype template, combined with a parametric warp mapping used to deform the original shape. The localization procedure is achieved using a multi-stage, mult... | Medical Image Segmentation and Localization using Deformable Templates | 2,995 |
Nuclear medicine (NM) images inherently suffer from large amounts of noise and blur. The purpose of this research is to reduce the noise and blur while maintaining image integrity for improved diagnosis. The proposed solution is to increase image quality after the standard pre- and post-processing undertaken by a gamma... | Enhancement of Noisy Planar Nuclear Medicine Images using Mean Field
Annealing | 2,996 |
This paper explores a comparative study of both the linear and kernel implementations of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis, Linear Discriminant Analysis and Independent Component Analysis. The experimental proced... | An Independent Evaluation of Subspace Face Recognition Algorithms | 2,997 |
Subtraction of aligned images is a means to assess changes in a wide variety of clinical applications. In this paper we explore the information theoretical origin of Mutual Information (MI), which is based on Shannon's entropy.However, the interpretation of standard MI registration as a communication channel suggests t... | MI image registration using prior knowledge | 2,998 |
This article describes the implementation of a system designed to automatically detect the presence of pulmonary embolism in lung scans. These images are firstly segmented, before alignment and feature extraction using PCA. The neural network was trained using the Hybrid Monte Carlo method, resulting in a committee of ... | Automatic Detection of Pulmonary Embolism using Computational
Intelligence | 2,999 |
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