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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Range Map with Missing Data - Joint Resolution Enhancement and Inpainting

Range Map with Missing Data - Joint Resolution Enhancement a...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Arnav V. Bhavsar Ambasamudram N. Rajagopalan Department of Electrical Engineering IIT Madras India

Range images captured from range scanning devices such as laser scanners or PMD (photonic mixer device) cameras, often possess drawbacks of having low resolution and/or missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections etc. In this work, we address both the issues in a single framework. We employ Bayesian regularization for resolution enhancement and inpainting in a general multi-image super-resolution scenario. We modify the traditional image formation model used in image/range super-resolution to account for the missing regions. This modification is important to couple the inpainting process with super-resolution. We also stress the importance of prior information in the integration and note that we require the priors to constrain the solution differently for inpainting and for super-resolution. The proposed inhomogeneous prior handles the requirements for inpainting as well as super-resolution. The modification of the imaging model and the formulation of the inhomogeneous prior are both important for the success of the integration. Our results show inpainting of large missing regions, reduction in distortions and good preservation of details at the high-resolution.

关键词： Optoelectronic and photonic sensors image resolution Cameras Reflectivity image sensors Bayesian methods Stress High-resolution imaging

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Change Detection and Object Segmentation: A Histogram of Features-Based Energy Minimization Approach

Change Detection and Object Segmentation: A Histogram of Fea...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Nilanjan Ray Baidya Nath Saha Hong Zhang Department of Computing Science University of Alberta Canada

We consider here a change detection problem: to find regions of change on a test image with respect to a reference image. Unlike the state-of-the-art change detection and background subtraction algorithms that compute only local (pixel location-based) changes, we propose to minimize a novel region-based energy functional based on Bhattacharya coefficient involving histograms of image features. The optimization of the proposed energy functional simply consists of two very efficient searches if a crude segmentation such as a bounding box around the region of change is sufficient. Also, it allows variational optimization via level set-based curve evolution for supervised binary image labeling. The framework is demonstrated to cope well with considerable camera motion and shifts of objects between the test and the reference images. We illustrate encouraging results on finding bounding box around abnormality from brain MRI, object detection for maritime surveillance, and segmenting oil-sand particles from conveyor belt images.

关键词： Object detection Object segmentation Histograms Testing image segmentation Change detection algorithms Pixel Labeling Cameras Magnetic resonance imaging

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A Switched Predictive Coding Method for Lossless Video Coding

A Switched Predictive Coding Method for Lossless Video Codin...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Anil Kumar Tiwari R V Raja Kumar LNM Institute of Information Technology Jaipur India Indian Institute of Technology Kharagpur Kharagpur India

This paper presents a switched predictive coding method for lossless compression of video. In the proposed method, a set of switched predictors is found by a training process that uses only a small number of successive frames of a video and then the trained predictors are used with a large number of the frames of the video. To find the predictors, the pixels of the successive frames are first classified based on an estimate of activity level in their neighbouring pixels and then LS based feedback type of predictors are estimated for all the pixels belonging to each of the classes. We propose a total of 21 classes, which are obtained by combining the seven slope bins of gradient adjusted predictor (GAP) and three classified temporal contexts. After collecting the predictors for pixels belonging to each of the 21 classes, the best predictor, in terms of minimum zero-order entropy, is chosen to represent the various classes. Simulation results show that the application of the set of the predictors results in competitive performance with the LOPT - one of the best methods in terms of achievable compression ratio. Our method and LOPT has same order of coding complexity while our decoder is computationally very simple as against high complexity of LOPT based decoder.

关键词： Predictive coding Video coding image coding Video compression Flyback transformers Decoding Feedback Amplitude estimation Motion estimation computer vision

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Gesture Based English Character Recognition for Human Machine Interaction in Interactive Set Top Box Using Multi-factor Analysis

Gesture Based English Character Recognition for Human Machin...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Tanushyam Chattopadhyay Provat Biswas Biswanath Saha Arpan Pal Convergence Solution Practices Tata Consultancy Services Limited Kolkata India

In this paper authors have presented a novel approach for human machine interaction (HMI) that can be used to give alphabets as input to Internet browser in an interactive Set top box with this added facility. The device is designed with a MEMS sensor which can be worn on fingertip to send alphabetical input to an interactive set top box. Users write the English alphabet in capital letter by just using the sensor attached to their hand. Then this data is undergone some filtering and finally it is sent to the recognition module. The features used for recognition consists of chain code based and un-directional un-weighted graph based features. Each of them recognizes the input character with some confidence factor lying in the closed interval (0,1). Final decision about the recognized character is made by using multifactorial approach. We get a recognition accuracy of more than 90%. We have tested the system with the inputs given by 22 users (16 male and 6 female) each of the user is asked to write the each letter 5 times.

关键词： Character recognition Humans Internet Hidden Markov models TV Keyboards Mice Handicapped aids computer vision computer graphics

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An Adaptive Clustering Based Non-linear Filter for the Restoration of Impulse Corrupted Digital images

An Adaptive Clustering Based Non-linear Filter for the Resto...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： S. Saudia Justin Varghese Krishnan Nallaperumal Santhosh P Mathew S. Allwin S.K. Thakur Centre for Information Technology & Engineering Manonmaniam Sundaranar University Tirunelveli Tamil Nadu India Department of Computer Science & Engineering Infant Jesus College of Engineering and Technology Thoothukudi Tamil Nadu India Department of Computer Science & Engineering SAINTGITS College of Engineering Kottayam Kerala India Naval Research Board New Delhi India

The paper proposes a 2D impulse filtering algorithm for the restoration of salt & pepper impulse corrupted digital images. The algorithm incorporates a hard-C means clustering stage to facilitate pixel classification in the impulse detection phase and an efficient adaptive filtering scheme in the restoration phase. The impulse detection scheme of the algorithm avoids mis-classification of signals as impulses by clearly distinguishing the high frequency image details from impulse corrupted pixels. The adaptive restoration phase identifies the most suitable signal restorer from among the true signals of a reliable neighborhood. Experimental results in terms of subjective assessment and objective metrics favor the proposed algorithm at all impulse noise levels over many top-ranking filters.

关键词： Adaptive filters Digital filters image restoration Digital images Signal restoration Filtering algorithms Clustering algorithms Signal processing Phase detection Frequency

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Subjective Performance of Texture Based Algorithm for Face Verification: The Role of Databases

Subjective Performance of Texture Based Algorithm for Face V...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： R. Raghavendra Ashok Rao G. Hemantha Kumar Department of studies in Computer Science University of Mysore Mysore India Dept of Computer Science J.S.S. College of Arts Commerce and Science Mysore India

This paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. The proposed method consists of three parts. The first part is a Log-Gabor filtering on facial image. The second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. The third part is transforming the set of features using subspace methods such as principal component analysis (PCA) and independent component analysis (ICA). Thus, in this paper two methods namely Log-Gabor mixture model (LGMM) based on PCA and Log-Gabor mixture model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. The nature of type & size of databases chosen on the performance of these algorithms are also studied. The experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms.

关键词： Gabor filters image databases Principal component analysis Independent component analysis Face recognition Frequency computer science Feature extraction Kernel Transfer functions

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Information Slicing: An Application to Object Classification in Satellite images

Information Slicing: An Application to Object Classification...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Hina Shah Suman Mitra Asim Banerjee Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India

Classification procedure aims at finding regions of the classes in the feature space. There are several algorithms proposed with supervised and unsupervised strategies for classification in literature. This paper goes on to propose a supervised method of classification using information slicing. Information lies in the feature space of the data to be classified. Training stage consists of slicing of the information by continuous partitioning of the feature space to find pure regions for classes from training data set. Classification then becomes a find-and-assign problem in the feature space for the input data. It has been shown here that this method of classification works well on data which have highly overlapping regions amongst the classes. This classification method is further applied for object classification in satellite imagery, where objects are homogeneous regions in images of considerably high resolution. Identification of homogeneous regions is done by a graph based segmentation algorithm which searches for segments having high intra region pixel similarity and high inter-region pixel dissimilarity. Feature vectors for each of these objects are calculated and then these feature vectors are given as an input to the information slicing classifier. Ground truth however is needed for the training stage of the classifier which has been generated here by manual intervention. Experiments have been done on four classes and results show that the method of information slicing works well on selective classes.

关键词： image segmentation Humans Visual system computer vision computer graphics image processing Application software Artificial satellites Communications technology Electronic mail

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Objects from Animacy: Joint Discovery in Shape and Haar Feature Space

Objects from Animacy: Joint Discovery in Shape and Haar Feat...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Sudipto Nandi Prithwijit Guha K.S. Venkatesh Department of Electrical Engineering Indian Institute of Technology Kanpur India

We propose that appearance descriptors derived from the complete animacy of an object during its scene presence more comprehensively capture the essence of an object than descriptors that merely encode uncorrelated sets of its instantaneous appearances. During its frame presence, an object presents itself in many poses with differing frequencies, thus generating multiple modes of varying strengths in the appearance feature space. Further, we utilize tracking information to extract the set of all appearances of the object, while excluding those intervals where the object is partly or fully occluded by other objects or background entities. This allows for completely unsupervised computation of the descriptors that consist of time-indexed vectors from shape and Haar feature templates which are then clustered to obtain appearance modes. These lead to the construction of object-animacy models as probability distributions over the space of co-occurrent shape and Haar templates. These object models are clustered further in an unsupervised manner by using different spatial clustering algorithms with a Bhattacharya distance metric between object models. Unsupervised categorization results on simple (PETS2000) and complex traffic scenes consisting of a wide variety of objects show robust performance of the proposed approach.

关键词： Shape Object detection Layout Clustering algorithms Robustness computer vision Space technology Probability distribution Traffic control computer graphics

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On Automatic Authenticity Verification of Printed Security Documents

On Automatic Authenticity Verification of Printed Security D...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Utpal Garain Biswajit Halder Indian Statistical Institute Kolkata India PDSIT Bengal Engineering and Science University Shibpur West Bengal India

This paper presents a pioneering effort to involve machine in checking document authenticity. A particular class of security documents has been considered for the present experiment. Bank cheques, several kinds of tickets like lottery tickets, air tickets, etc., legal deeds, certificates, mark sheets, postal stamps, etc. all these documents fall under the same class as far as security is concerned. Criminalspsila efforts for generating fraudulent version of such documents are on the rise. This study attempts to develop a general framework for automatic authenticity verification of such security documents. The proposed method first computationally extracts the security features from the document images and then the notion of authenticity vs. duplicity is defined in the feature space. Bank cheques are taken as a reference for conducting experiment. Support vector machines (SVMs) are used to verify authenticity of these cheques. Non-linear kernel functions are used to conduct this experiment. Results show that a polynomial kernel based SVM gives about 99.5% accuracy discriminating duplicate cheques from genuine ones. This strongly attests the viability of the proposed approach for machine authentication of printed security documents.

关键词： Security Authentication Law Legal factors Feature extraction Support vector machines Kernel Forensics Printing computer vision

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Edge Detection in Speckled SAR images with Improved ROEWA

Edge Detection in Speckled SAR Images with Improved ROEWA

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Jennifer Ranjani M. Gokila S.J. Thiruvengadam Department of Information Technology Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India

Automatic detection of sustained intensity changes (edges) in SAR images for applications requiring localization and identification of objects is complicated by the nature of the speckle. The speckle is characterized by a high degree of correlation and multiplicative signal dependence. Ratio based detectors like MRoA and RGoA edge detector use predefined thresholds. The modified RGoA detector defines an automatic threshold determining method. But all these edge detectors, apart from detecting the object edges, detect a number of false edges. In this paper, a new ROEWA based algorithm that automatically discriminates the object boundaries and the false edges is proposed. Otsupsilas nonparametric and unsupervised principle for automatic threshold selection is introduced in this classification process. An optimal threshold is selected by maximizing the seperability of the classes in gray level. Real SAR images are used to verify our method and the results are compared with the existing methods for edge detection. Experimental results show that the proposed method is robust and efficient.

关键词： image edge detection Detectors Speckle Object detection Optical scattering Reflectivity Radar scattering Radar detection image segmentation Histograms

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