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24,802 | Egocentric Pose Recognition in Four Lines of Code | cs.CV | We tackle the problem of estimating the 3D pose of an individual's upper
limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider
pose estimation during everyday interactions with objects. Past work shows that
strong pose+viewpoint priors and depth-based features are crucial for robust
performance... | computer science |
24,803 | A Bayesian Framework for Sparse Representation-Based 3D Human Pose
Estimation | cs.CV | A Bayesian framework for 3D human pose estimation from monocular images based
on sparse representation (SR) is introduced. Our probabilistic approach aims at
simultaneously learning two overcomplete dictionaries (one for the visual input
space and the other for the pose space) with a shared sparse representation.
Exist... | computer science |
24,804 | 3D Hand Pose Detection in Egocentric RGB-D Images | cs.CV | We focus on the task of everyday hand pose estimation from egocentric
viewpoints. For this task, we show that depth sensors are particularly
informative for extracting near-field interactions of the camera wearer with
his/her environment. Despite the recent advances in full-body pose estimation
using Kinect-like sensor... | computer science |
24,805 | Pedestrian Detection aided by Deep Learning Semantic Tasks | cs.CV | Deep learning methods have achieved great success in pedestrian detection,
owing to its ability to learn features from raw pixels. However, they mainly
capture middle-level representations, such as pose of pedestrian, but confuse
positive with hard negative samples, which have large ambiguity, e.g. the shape
and appear... | computer science |
24,806 | Color image quality assessment measure using multivariate generalized
Gaussian distribution | cs.CV | This paper deals with color image quality assessment in the reduced-reference
framework based on natural scenes statistics. In this context, we propose to
model the statistics of the steerable pyramid coefficients by a Multivariate
Generalized Gaussian distribution (MGGD). This model allows taking into account
the high... | computer science |
24,807 | Robust Camera Location Estimation by Convex Programming | cs.CV | $3$D structure recovery from a collection of $2$D images requires the
estimation of the camera locations and orientations, i.e. the camera motion.
For large, irregular collections of images, existing methods for the location
estimation part, which can be formulated as the inverse problem of estimating
$n$ locations $\m... | computer science |
24,808 | A Clearer Picture of Blind Deconvolution | cs.CV | Blind deconvolution is the problem of recovering a sharp image and a blur
kernel from a noisy blurry image. Recently, there has been a significant effort
on understanding the basic mechanisms to solve blind deconvolution. While this
effort resulted in the deployment of effective algorithms, the theoretical
findings gen... | computer science |
24,809 | Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels | cs.CV | In this paper, we develop an approach to exploiting kernel methods with
manifold-valued data. In many computer vision problems, the data can be
naturally represented as points on a Riemannian manifold. Due to the
non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision
and machine learning algori... | computer science |
24,810 | Untangling Local and Global Deformations in Deep Convolutional Networks
for Image Classification and Sliding Window Detection | cs.CV | Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling'
(MP) layers to extract deformation-invariant features, but we argue in favor of
a more refined treatment. First, we introduce epitomic convolution as a
building block alternative to the common convolution-MP cascade of DCNNs; while
having ide... | computer science |
24,811 | Recovering Spatiotemporal Correspondence between Deformable Objects by
Exploiting Consistent Foreground Motion in Video | cs.CV | Given unstructured videos of deformable objects, we automatically recover
spatiotemporal correspondences to map one object to another (such as animals in
the wild). While traditional methods based on appearance fail in such
challenging conditions, we exploit consistency in object motion between
instances. Our approach ... | computer science |
24,812 | Orthogonal Matrix Retrieval in Cryo-Electron Microscopy | cs.CV | In single particle reconstruction (SPR) from cryo-electron microscopy
(cryo-EM), the 3D structure of a molecule needs to be determined from its 2D
projection images taken at unknown viewing directions. Zvi Kam showed already
in 1980 that the autocorrelation function of the 3D molecule over the rotation
group SO(3) can ... | computer science |
24,813 | Material Recognition in the Wild with the Materials in Context Database | cs.CV | Recognizing materials in real-world images is a challenging task. Real-world
materials have rich surface texture, geometry, lighting conditions, and
clutter, which combine to make the problem particularly difficult. In this
paper, we introduce a new, large-scale, open dataset of materials in the wild,
the Materials in ... | computer science |
24,814 | Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit | cs.CV | Sparse approximations using highly over-complete dictionaries is a
state-of-the-art tool for many imaging applications including denoising,
super-resolution, compressive sensing, light-field analysis, and object
recognition. Unfortunately, the applicability of such methods is severely
hampered by the computational burd... | computer science |
24,815 | Learning Spatiotemporal Features with 3D Convolutional Networks | cs.CV | We propose a simple, yet effective approach for spatiotemporal feature
learning using deep 3-dimensional convolutional networks (3D ConvNets) trained
on a large scale supervised video dataset. Our findings are three-fold: 1) 3D
ConvNets are more suitable for spatiotemporal feature learning compared to 2D
ConvNets; 2) A... | computer science |
24,816 | Feedforward semantic segmentation with zoom-out features | cs.CV | We introduce a purely feed-forward architecture for semantic segmentation. We
map small image elements (superpixels) to rich feature representations
extracted from a sequence of nested regions of increasing extent. These regions
are obtained by "zooming out" from the superpixel all the way to scene-level
resolution. Th... | computer science |
24,817 | Fast Steerable Principal Component Analysis | cs.CV | Cryo-electron microscopy nowadays often requires the analysis of hundreds of
thousands of 2D images as large as a few hundred pixels in each direction. Here
we introduce an algorithm that efficiently and accurately performs principal
component analysis (PCA) for a large set of two-dimensional images, and, for
each imag... | computer science |
24,818 | Analytical Comparison of Noise Reduction Filters for Image Restoration
Using SNR Estimation | cs.CV | Noise removal from images is a part of image restoration in which we try to
reconstruct or recover an image that has been degraded by using apriori
knowledge of the degradation phenomenon. Noises present in images can be of
various types with their characteristic Probability Distribution Functions
(PDF). Noise removal ... | computer science |
24,819 | Hashing on Nonlinear Manifolds | cs.CV | Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes preserving
the Euclidean similarity in the original space. Manifold learning techniques,
in contra... | computer science |
24,820 | Covariance estimation using conjugate gradient for 3D classification in
Cryo-EM | cs.CV | Classifying structural variability in noisy projections of biological
macromolecules is a central problem in Cryo-EM. In this work, we build on a
previous method for estimating the covariance matrix of the three-dimensional
structure present in the molecules being imaged. Our proposed method allows for
incorporation of... | computer science |
24,821 | DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour
Detection | cs.CV | Contour detection has been a fundamental component in many image segmentation
and object detection systems. Most previous work utilizes low-level features
such as texture or saliency to detect contours and then use them as cues for a
higher-level task such as object detection. However, we claim that recognizing
objects... | computer science |
24,822 | Detector Discovery in the Wild: Joint Multiple Instance and
Representation Learning | cs.CV | We develop methods for detector learning which exploit joint training over
both weak and strong labels and which transfer learned perceptual
representations from strongly-labeled auxiliary tasks. Previous methods for
weak-label learning often learn detector models independently using latent
variable optimization, but f... | computer science |
24,823 | Gradient Boundary Histograms for Action Recognition | cs.CV | This paper introduces a high efficient local spatiotemporal descriptor,
called gradient boundary histograms (GBH). The proposed GBH descriptor is built
on simple spatio-temporal gradients, which are fast to compute. We demonstrate
that it can better represent local structure and motion than other
gradient-based descrip... | computer science |
24,824 | Simple Two-Dimensional Object Tracking based on a Graph Algorithm | cs.CV | The visual observation and tracking of cells and other micrometer-sized
objects has many different biomedical applications. The automation of those
tasks based on computer methods helps in the evaluation of such measurements.
In this work, we present a general purpose algorithm that excels at evaluating
deterministic b... | computer science |
24,825 | Deeply learned face representations are sparse, selective, and robust | cs.CV | This paper designs a high-performance deep convolutional network (DeepID2+)
for face recognition. It is learned with the identification-verification
supervisory signal. By increasing the dimension of hidden representations and
adding supervision to early convolutional layers, DeepID2+ achieves new
state-of-the-art on L... | computer science |
24,826 | Convolutional Feature Masking for Joint Object and Stuff Segmentation | cs.CV | The topic of semantic segmentation has witnessed considerable progress due to
the powerful features learned by convolutional neural networks (CNNs). The
current leading approaches for semantic segmentation exploit shape information
by extracting CNN features from masked image regions. This strategy introduces
artificia... | computer science |
24,827 | Scalable, High-Quality Object Detection | cs.CV | Current high-quality object detection approaches use the scheme of
salience-based object proposal methods followed by post-classification using
deep convolutional features. This spurred recent research in improving object
proposal methods. However, domain agnostic proposal generation has the
principal drawback that the... | computer science |
24,828 | Memory Bounded Deep Convolutional Networks | cs.CV | In this work, we investigate the use of sparsity-inducing regularizers during
training of Convolution Neural Networks (CNNs). These regularizers encourage
that fewer connections in the convolution and fully connected layers take
non-zero values and in effect result in sparse connectivity between hidden
units in the dee... | computer science |
24,829 | Event Retrieval Using Motion Barcodes | cs.CV | We introduce a simple and effective method for retrieval of videos showing a
specific event, even when the videos of that event were captured from
significantly different viewpoints. Appearance-based methods fail in such
cases, as appearances change with large changes of viewpoints.
Our method is based on a pixel-bas... | computer science |
24,830 | Textural Approach for Mass Abnormality Segmentation in Mammographic
Images | cs.CV | Mass abnormality segmentation is a vital step for the medical diagnostic
process and is attracting more and more the interest of many research groups.
Currently, most of the works achieved in this area have used the Gray Level
Co-occurrence Matrix (GLCM) as texture features with a region-based approach.
These features ... | computer science |
24,831 | Parsing Occluded People by Flexible Compositions | cs.CV | This paper presents an approach to parsing humans when there is significant
occlusion. We model humans using a graphical model which has a tree structure
building on recent work [32, 6] and exploit the connectivity prior that, even
in presence of occlusion, the visible nodes form a connected subtree of the
graphical mo... | computer science |
24,832 | Convolutional Neural Networks at Constrained Time Cost | cs.CV | Though recent advanced convolutional neural networks (CNNs) have been
improving the image recognition accuracy, the models are getting more complex
and time-consuming. For real-world applications in industrial and commercial
scenarios, engineers and developers are often faced with the requirement of
constrained time bu... | computer science |
24,833 | Reading Text in the Wild with Convolutional Neural Networks | cs.CV | In this work we present an end-to-end system for text spotting -- localising
and recognising text in natural scene images -- and text based image retrieval.
This system is based on a region proposal mechanism for detection and deep
convolutional neural networks for recognition. Our pipeline uses a novel
combination of ... | computer science |
24,834 | Person Re-identification by Saliency Learning | cs.CV | Human eyes can recognize person identities based on small salient regions,
i.e. human saliency is distinctive and reliable in pedestrian matching across
disjoint camera views. However, such valuable information is often hidden when
computing similarities of pedestrian images with existing approaches. Inspired
by our us... | computer science |
24,835 | Background Modelling using Octree Color Quantization | cs.CV | By assuming that the most frequently occuring color in a video or a region of
a video I propose a new algorithm for detecting foreground objects in a video.
The process of detecting the foreground objects is complicated because of the
fact that there may be swaying trees, objects of the background being moved
around or... | computer science |
24,836 | CoMIC: Good features for detection and matching at object boundaries | cs.CV | Feature or interest points typically use information aggregation in 2D
patches which does not remain stable at object boundaries when there is object
motion against a significantly varying background. Level or iso-intensity
curves are much more stable under such conditions, especially the longer ones.
In this paper, we... | computer science |
24,837 | Risk Estimation Without Using Stein's Lemma -- Application to Image
Denoising | cs.CV | We address the problem of image denoising in additive white noise without
placing restrictive assumptions on its statistical distribution. In the recent
literature, specific noise distributions have been considered and
correspondingly, optimal denoising techniques have been developed. One of the
successful approaches f... | computer science |
24,838 | Deep Visual-Semantic Alignments for Generating Image Descriptions | cs.CV | We present a model that generates natural language descriptions of images and
their regions. Our approach leverages datasets of images and their sentence
descriptions to learn about the inter-modal correspondences between language
and visual data. Our alignment model is based on a novel combination of
Convolutional Neu... | computer science |
24,839 | Bayesian Image Restoration for Poisson Corrupted Image using a Latent
Variational Method with Gaussian MRF | cs.CV | We treat an image restoration problem with a Poisson noise chan- nel using a
Bayesian framework. The Poisson randomness might be appeared in observation of
low contrast object in the field of imaging. The noise observation is often
hard to treat in a theo- retical analysis. In our formulation, we interpret the
observat... | computer science |
24,840 | An Approach for Reducing Outliers of Non Local Means Image Denoising
Filter | cs.CV | We propose an adaptive approach for non local means (NLM) image filtering
termed as non local adaptive clipped means (NLACM), which reduces the effect of
outliers and improves the denoising quality as compared to traditional NLM.
Common method to neglect outliers from a data population is computation of mean
in a range... | computer science |
24,841 | Actions and Attributes from Wholes and Parts | cs.CV | We investigate the importance of parts for the tasks of action and attribute
classification. We develop a part-based approach by leveraging convolutional
network features inspired by recent advances in computer vision. Our part
detectors are a deep version of poselets and capture parts of the human body
under a distinc... | computer science |
24,842 | Image quality assessment measure based on natural image statistics in
the Tetrolet domain | cs.CV | This paper deals with a reduced reference (RR) image quality measure based on
natural image statistics modeling. For this purpose, Tetrolet transform is used
since it provides a convenient way to capture local geometric structures. This
transform is applied to both reference and distorted images. Then, Gaussian
Scale M... | computer science |
24,843 | Joint Segmentation and Deconvolution of Ultrasound Images Using a
Hierarchical Bayesian Model based on Generalized Gaussian Priors | cs.CV | This paper proposes a joint segmentation and deconvolution Bayesian method
for medical ultrasound (US) images. Contrary to piecewise homogeneous images,
US images exhibit heavy characteristic speckle patterns correlated with the
tissue structures. The generalized Gaussian distribution (GGD) has been shown
to be one of ... | computer science |
24,844 | Cancer Detection with Multiple Radiologists via Soft Multiple Instance
Logistic Regression and $L_1$ Regularization | cs.CV | This paper deals with the multiple annotation problem in medical application
of cancer detection in digital images. The main assumption is that though
images are labeled by many experts, the number of images read by the same
expert is not large. Thus differing with the existing work on modeling each
expert and ground t... | computer science |
24,845 | Brain Tumor Detection Based on Bilateral Symmetry Information | cs.CV | Advances in computing technology have allowed researchers across many fields
of endeavor to collect and maintain vast amounts of observational statistical
data such as clinical data,biological patient data,data regarding access of web
sites,financial data,and the like.Brain Magnetic Resonance
Imaging(MRI)segmentation i... | computer science |
24,846 | Road Detection via On--line Label Transfer | cs.CV | Vision-based road detection is an essential functionality for supporting
advanced driver assistance systems (ADAS) such as road following and vehicle
and pedestrian detection. The major challenges of road detection are dealing
with shadows and lighting variations and the presence of other objects in the
scene. Current ... | computer science |
24,847 | Object-centric Sampling for Fine-grained Image Classification | cs.CV | This paper proposes to go beyond the state-of-the-art deep convolutional
neural network (CNN) by incorporating the information from object detection,
focusing on dealing with fine-grained image classification. Unfortunately, CNN
suffers from over-fiting when it is trained on existing fine-grained image
classification b... | computer science |
24,848 | Candidate Constrained CRFs for Loss-Aware Structured Prediction | cs.CV | When evaluating computer vision systems, we are often concerned with
performance on a task-specific evaluation measure such as the
Intersection-Over-Union score used in the PASCAL VOC image segmentation
challenge. Ideally, our systems would be tuned specifically to these evaluation
measures. However, despite much work ... | computer science |
24,849 | Multi-Atlas Segmentation of Biomedical Images: A Survey | cs.CV | Multi-atlas segmentation (MAS), first introduced and popularized by the
pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh,
Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert
and Hammers (2006), is becoming one of the most widely-used and successful
image segmentati... | computer science |
24,850 | Deep Domain Confusion: Maximizing for Domain Invariance | cs.CV | Recent reports suggest that a generic supervised deep CNN model trained on a
large-scale dataset reduces, but does not remove, dataset bias on a standard
benchmark. Fine-tuning deep models in a new domain can require a significant
amount of data, which for many applications is simply not available. We propose
a new CNN... | computer science |
24,851 | Road Detection by One-Class Color Classification: Dataset and
Experiments | cs.CV | Detecting traversable road areas ahead a moving vehicle is a key process for
modern autonomous driving systems. A common approach to road detection consists
of exploiting color features to classify pixels as road or background. These
algorithms reduce the effect of lighting variations and weather conditions by
exploiti... | computer science |
24,852 | A Novel Adaptive Possibilistic Clustering Algorithm | cs.CV | In this paper a novel possibilistic c-means clustering algorithm, called
Adaptive Possibilistic c-means, is presented. Its main feature is that {\it
all} its parameters, after their initialization, are properly adapted during
its execution. Provided that the algorithm starts with a reasonable
overestimate of the number... | computer science |
24,853 | An active search strategy for efficient object class detection | cs.CV | Object class detectors typically apply a window classifier to all the windows
in a large set, either in a sliding window manner or using object proposals. In
this paper, we develop an active search strategy that sequentially chooses the
next window to evaluate based on all the information gathered before. This
results ... | computer science |
24,854 | Edge Preserving Multi-Modal Registration Based On Gradient Intensity
Self-Similarity | cs.CV | Image registration is a challenging task in the world of medical imaging.
Particularly, accurate edge registration plays a central role in a variety of
clinical conditions. The Modality Independent Neighbourhood Descriptor (MIND)
demonstrates state of the art alignment, based on the image self-similarity.
However, this... | computer science |
24,855 | An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation | cs.CV | In this paper, an automatic seeded region growing algorithm is proposed for
cellular image segmentation. First, the regions of interest (ROIs) extracted
from the preprocessed image. Second, the initial seeds are automatically
selected based on ROIs extracted from the image. Third, the most reprehensive
seeds are select... | computer science |
24,856 | Representing Data by a Mixture of Activated Simplices | cs.CV | We present a new model which represents data as a mixture of simplices.
Simplices are geometric structures that generalize triangles. We give a simple
geometric understanding that allows us to learn a simplicial structure
efficiently. Our method requires that the data are unit normalized (and thus
lie on the unit spher... | computer science |
24,857 | Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite
Matrices | cs.CV | Symmetric Positive Definite (SPD) matrices have become popular to encode
image information. Accounting for the geometry of the Riemannian manifold of
SPD matrices has proven key to the success of many algorithms. However, most
existing methods only approximate the true shape of the manifold locally by its
tangent plane... | computer science |
24,858 | A Framework for Shape Analysis via Hilbert Space Embedding | cs.CV | We propose a framework for 2D shape analysis using positive definite kernels
defined on Kendall's shape manifold. Different representations of 2D shapes are
known to generate different nonlinear spaces. Due to the nonlinearity of these
spaces, most existing shape classification algorithms resort to nearest
neighbor met... | computer science |
24,859 | Optimizing Over Radial Kernels on Compact Manifolds | cs.CV | We tackle the problem of optimizing over all possible positive definite
radial kernels on Riemannian manifolds for classification. Kernel methods on
Riemannian manifolds have recently become increasingly popular in computer
vision. However, the number of known positive definite kernels on manifolds
remain very limited.... | computer science |
24,860 | Oriented Edge Forests for Boundary Detection | cs.CV | We present a simple, efficient model for learning boundary detection based on
a random forest classifier. Our approach combines (1) efficient clustering of
training examples based on simple partitioning of the space of local edge
orientations and (2) scale-dependent calibration of individual tree output
probabilities p... | computer science |
24,861 | A survey of modern optical character recognition techniques | cs.CV | This report explores the latest advances in the field of digital document
recognition. With the focus on printed document imagery, we discuss the major
developments in optical character recognition (OCR) and document image
enhancement/restoration in application to Latin and non-Latin scripts. In
addition, we review and... | computer science |
24,862 | Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in
the Homography Space | cs.CV | With the aim to improve the performance of feature matching, we present an
unsupervised approach to fuse various local descriptors in the space of
homographies. Inspired by the observation that the homographies of correct
feature correspondences vary smoothly along the spatial domain, our approach
stands on the unsuper... | computer science |
24,863 | The application of the Bayes Ying Yang harmony based GMMs in on-line
signature verification | cs.CV | In this contribution, a Bayes Ying Yang(BYY) harmony based approach for
on-line signature verification is presented. In the proposed method, a simple
but effective Gaussian Mixture Models(GMMs) is used to represent for each
user's signature model based on the prior information collected. Different from
the early works,... | computer science |
24,864 | A Study of Sindhi Related and Arabic Script Adapted languages
Recognition | cs.CV | A large number of publications are available for the Optical Character
Recognition (OCR). Significant researches, as well as articles are present for
the Latin, Chinese and Japanese scripts. Arabic script is also one of mature
script from OCR perspective. The adaptive languages which share Arabic script
or its extended... | computer science |
24,865 | Combining the Best of Graphical Models and ConvNets for Semantic
Segmentation | cs.CV | We present a two-module approach to semantic segmentation that incorporates
Convolutional Networks (CNNs) and Graphical Models. Graphical models are used
to generate a small (5-30) set of diverse segmentations proposals, such that
this set has high recall. Since the number of required proposals is so low, we
can extrac... | computer science |
24,866 | Inexact Alternating Direction Method Based on Newton descent algorithm
with Application to Poisson Image Deblurring | cs.CV | The recovery of images from the observations that are degraded by a linear
operator and further corrupted by Poisson noise is an important task in modern
imaging applications such as astronomical and biomedical ones. Gradient-based
regularizers involve the popular total variation semi-norm have become standard
techniqu... | computer science |
24,867 | Fixed Point Algorithm Based on Quasi-Newton Method for Convex
Minimization Problem with Application to Image Deblurring | cs.CV | Solving an optimization problem whose objective function is the sum of two
convex functions has received considerable interests in the context of image
processing recently. In particular, we are interested in the scenario when a
non-differentiable convex function such as the total variation (TV) norm is
included in the... | computer science |
24,868 | Automatic video scene segmentation based on spatial-temporal clues and
rhythm | cs.CV | With ever increasing computing power and data storage capacity, the potential
for large digital video libraries is growing rapidly.However, the massive use
of video for the moment is limited by its opaque characteristics. Indeed, a
user who has to handle and retrieve sequentially needs too much time in order
to find ou... | computer science |
24,869 | Highly Efficient Forward and Backward Propagation of Convolutional
Neural Networks for Pixelwise Classification | cs.CV | We present highly efficient algorithms for performing forward and backward
propagation of Convolutional Neural Network (CNN) for pixelwise classification
on images. For pixelwise classification tasks, such as image segmentation and
object detection, surrounding image patches are fed into CNN for predicting the
classes ... | computer science |
24,870 | Discovering beautiful attributes for aesthetic image analysis | cs.CV | Aesthetic image analysis is the study and assessment of the aesthetic
properties of images. Current computational approaches to aesthetic image
analysis either provide accurate or interpretable results. To obtain both
accuracy and interpretability by humans, we advocate the use of learned and
nameable visual attributes... | computer science |
24,871 | What is a salient object? A dataset and a baseline model for salient
object detection | cs.CV | Salient object detection or salient region detection models, diverging from
fixation prediction models, have traditionally been dealing with locating and
segmenting the most salient object or region in a scene. While the notion of
most salient object is sensible when multiple objects exist in a scene, current
datasets ... | computer science |
24,872 | A Robust Regression Approach for Background/Foreground Segmentation | cs.CV | Background/foreground segmentation has a lot of applications in image and
video processing. In this paper, a segmentation algorithm is proposed which is
mainly designed for text and line extraction in screen content. The proposed
method makes use of the fact that the background in each block is usually
smoothly varying... | computer science |
24,873 | Iranian cashes recognition using mobile | cs.CV | In economical societies of today, using cash is an inseparable aspect of
human life. People use cashes for marketing, services, entertainments, bank
operations and so on. This huge amount of contact with cash and the necessity
of knowing the monetary value of it caused one of the most challenging problems
for visually ... | computer science |
24,874 | An Algebraical Model for Gray Level Images | cs.CV | In this paper we propose a new algebraical model for the gray level images.
It can be used for digital image processing. The model adresses to those images
which are generated in improper light conditions (very low or high level). The
vector space structure is able to illustrate some features into the image using
modif... | computer science |
24,875 | Color Image Enhancement In the Framework of Logarithmic Models | cs.CV | In this paper, we propose a mathematical model for color image processing. It
is a logarithmical one. We consider the cube (-1,1)x(-1,1)x(-1,1) as the set of
values for the color space. We define two operations: addition <+> and real
scalar multiplication <x>. With these operations the space of colors becomes a
real ve... | computer science |
24,876 | A Mathematical Model for Logarithmic Image Processing | cs.CV | In this paper, we propose a new mathematical model for image processing. It
is a logarithmical one. We consider the bounded interval (-1, 1) as the set of
gray levels. Firstly, we define two operations: addition <+> and real scalar
multiplication <x>. With these operations, the set of gray levels becomes a
real vector ... | computer science |
24,877 | The Affine Transforms for Image Enhancement in the Context of
Logarithmic Models | cs.CV | The logarithmic model offers new tools for image processing. An efficient
method for image enhancement is to use an affine transformation with the
logarithmic operations: addition and scalar multiplication. We define some
criteria for automatically determining the parameters of the processing and
this is done via mean ... | computer science |
24,878 | Full-reference image quality assessment by combining global and local
distortion measures | cs.CV | Full-reference image quality assessment (FR-IQA) techniques compare a
reference and a distorted/test image and predict the perceptual quality of the
test image in terms of a scalar value representing an objective score. The
evaluation of FR-IQA techniques is carried out by comparing the objective
scores from the techni... | computer science |
24,879 | High Frequency Content based Stimulus for Perceptual Sharpness
Assessment in Natural Images | cs.CV | A blind approach to evaluate the perceptual sharpness present in a natural
image is proposed. Though the literature demonstrates a set of variegated
visual cues to detect or evaluate the absence or presence of sharpness, we
emphasize in the current work that high frequency content and local standard
deviation can form ... | computer science |
24,880 | Towards Open World Recognition | cs.CV | With the of advent rich classification models and high computational power
visual recognition systems have found many operational applications.
Recognition in the real world poses multiple challenges that are not apparent
in controlled lab environments. The datasets are dynamic and novel categories
must be continuously... | computer science |
24,881 | Decomposition-Based Domain Adaptation for Real-World Font Recognition | cs.CV | We present a domain adaption framework to address a domain mismatch between
synthetic training and real-world testing data. We demonstrate our method on a
challenging fine-grain classification problem: recognizing a font style from an
image of text. In this task, it is very easy to generate lots of rendered font
exampl... | computer science |
24,882 | Image Dynamic Range Enhancement in the Context of Logarithmic Models | cs.CV | Images of a scene observed under a variable illumination or with a variable
optical aperture are not identical. Does a privileged representant exist? In
which mathematical context? How to obtain it? The authors answer to such
questions in the context of logarithmic models for images. After a short
presentation of the m... | computer science |
24,883 | Gray level image enhancement using the Bernstein polynomials | cs.CV | This paper presents a method for enhancing the gray level images. This
presented method takes part from the category of point operations and it is
based on piecewise linear functions. The interpolation nodes of these functions
are calculated using the Bernstein polynomials. | computer science |
24,884 | Gray Level Image Enhancement Using Polygonal Functions | cs.CV | This paper presents a method for enhancing the gray level images. This method
takes part from the category of point transforms and it is based on
interpolation functions. The latter have a graphic represented by polygonal
lines. The interpolation nodes of these functions are calculated taking into
account the statistic... | computer science |
24,885 | Image Enhancement Using a Generalization of Homographic Function | cs.CV | This paper presents a new method of gray level image enhancement, based on
point transforms. In order to define the transform function, it was used a
generalization of the homographic function. | computer science |
24,886 | Contour Detection Using Contrast Formulas in the Framework of
Logarithmic Models | cs.CV | In this paper we use a new logarithmic model of image representation,
developed in [1,2], for edge detection. In fact, in the framework of the new
model we obtain the formulas for computing the "contrast of a pixel" and the
"contrast" image is just the "contour" or edge image. In our setting the range
of values is pres... | computer science |
24,887 | Minimizing the Number of Matching Queries for Object Retrieval | cs.CV | To increase the computational efficiency of interest-point based object
retrieval, researchers have put remarkable research efforts into improving the
efficiency of kNN-based feature matching, pursuing to match thousands of
features against a database within fractions of a second. However, due to the
high-dimensional n... | computer science |
24,888 | Deep Structured Output Learning for Unconstrained Text Recognition | cs.CV | We develop a representation suitable for the unconstrained recognition of
words in natural images: the general case of no fixed lexicon and unknown
length.
To this end we propose a convolutional neural network (CNN) based
architecture which incorporates a Conditional Random Field (CRF) graphical
model, taking the who... | computer science |
24,889 | Unsupervised Learning of Spatiotemporally Coherent Metrics | cs.CV | Current state-of-the-art classification and detection algorithms rely on
supervised training. In this work we study unsupervised feature learning in the
context of temporally coherent video data. We focus on feature learning from
unlabeled video data, using the assumption that adjacent video frames contain
semantically... | computer science |
24,890 | Fractional Max-Pooling | cs.CV | Convolutional networks almost always incorporate some form of spatial
pooling, and very often it is alpha times alpha max-pooling with alpha=2.
Max-pooling act on the hidden layers of the network, reducing their size by an
integer multiplicative factor alpha. The amazing by-product of discarding 75%
of your data is tha... | computer science |
24,891 | Image enhancement using the mean dynamic range maximization with
logarithmic operations | cs.CV | In this paper we use a logarithmic model for gray level image enhancement. We
begin with a short presentation of the model and then, we propose a new formula
for the mean dynamic range. After that we present two image transforms: one
performs an optimal enhancement of the mean dynamic range using the logarithmic
additi... | computer science |
24,892 | Semantic Part Segmentation using Compositional Model combining Shape and
Appearance | cs.CV | In this paper, we study the problem of semantic part segmentation for
animals. This is more challenging than standard object detection, object
segmentation and pose estimation tasks because semantic parts of animals often
have similar appearance and highly varying shapes. To tackle these challenges,
we build a mixture ... | computer science |
24,893 | Effective persistent homology of digital images | cs.CV | In this paper, three Computational Topology methods (namely effective
homology, persistent homology and discrete vector fields) are mixed together to
produce algorithms for homological digital image processing. The algorithms
have been implemented as extensions of the Kenzo system and have shown a good
performance when... | computer science |
24,894 | Automated Objective Surgical Skill Assessment in the Operating Room
Using Unstructured Tool Motion | cs.CV | Previous work on surgical skill assessment using intraoperative tool motion
in the operating room (OR) has focused on highly-structured surgical tasks such
as cholecystectomy. Further, these methods only considered generic motion
metrics such as time and number of movements, which are of limited instructive
value. In t... | computer science |
24,895 | Learning to Segment Moving Objects in Videos | cs.CV | We segment moving objects in videos by ranking spatio-temporal segment
proposals according to "moving objectness": how likely they are to contain a
moving object. In each video frame, we compute segment proposals using multiple
figure-ground segmentations on per frame motion boundaries. We rank them with a
Moving Objec... | computer science |
24,896 | Pooled Motion Features for First-Person Videos | cs.CV | In this paper, we present a new feature representation for first-person
videos. In first-person video understanding (e.g., activity recognition), it is
very important to capture both entire scene dynamics (i.e., egomotion) and
salient local motion observed in videos. We describe a representation framework
based on time... | computer science |
24,897 | Fracking Deep Convolutional Image Descriptors | cs.CV | In this paper we propose a novel framework for learning local image
descriptors in a discriminative manner. For this purpose we explore a siamese
architecture of Deep Convolutional Neural Networks (CNN), with a Hinge
embedding loss on the L2 distance between descriptors. Since a siamese
architecture uses pairs rather t... | computer science |
24,898 | Visual Instance Retrieval with Deep Convolutional Networks | cs.CV | This paper provides an extensive study on the availability of image
representations based on convolutional networks (ConvNets) for the task of
visual instance retrieval. Besides the choice of convolutional layers, we
present an efficient pipeline exploiting multi-scale schemes to extract local
features, in particular, ... | computer science |
24,899 | Visual Scene Representations: Contrast, Scaling and Occlusion | cs.CV | We study the structure of representations, defined as approximations of
minimal sufficient statistics that are maximal invariants to nuisance factors,
for visual data subject to scaling and occlusion of line-of-sight. We derive
analytical expressions for such representations and show that, under certain
restrictive ass... | computer science |
24,900 | The local low-dimensionality of natural images | cs.CV | We develop a new statistical model for photographic images, in which the
local responses of a bank of linear filters are described as jointly Gaussian,
with zero mean and a covariance that varies slowly over spatial position. We
optimize sets of filters so as to minimize the nuclear norms of matrices of
their local act... | computer science |
24,901 | Visualizing and Comparing Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) have achieved comparable error rates to
well-trained human on ILSVRC2014 image classification task. To achieve better
performance, the complexity of CNNs is continually increasing with deeper and
bigger architectures. Though CNNs achieved promising external classification
behavior, ... | computer science |
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