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

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Object Category Recognition with Projected Texture

Object Category Recognition with Projected Texture

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

作者： Avinash Sharma Anoop Namboodiri International Institute of Information Technology Center for Visual Information Technology Hyderabad India

Recognition of object categories from their images is extremely challenging due to the large intra-class variations, and variations in pose, illumination and scale, in addition to lack of depth information of the object. Recovering the depth information from multiple images or from image cues such as variations in illumination or focus, is both computationally intensive and error prone. In contrast, the appearance based approaches are more robust and computationally efficient. However, they lack the potential accuracy of 3D feature based approaches due to the lack of shape information. We propose the use of structured lighting patterns projected on the object, which gets deformed according to the shape of the object for recognition. Since our goal is object classification and not shape recovery, we characterize the deformations using simple texture measures, thus avoiding depth recovery step. Moreover, the shape information present in the deformations is implicitly used for classification. We show that the information thus derived can significantly improve the accuracy of object category recognition from arbitrary-pose images.

关键词： Robustness image recognition Lighting Focusing Information technology Pattern recognition Shape measurement Cameras Object recognition computer vision

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Adaptive Nonlinear image Denoising and Restoration Using a Cooperative Bayesian Estimation Approach

Adaptive Nonlinear Image Denoising and Restoration Using a C...

引用

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

作者： Akshaya. K. Mishra Alexander Wong David. A. Clausi Paul. W. Fieguth

A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzzy c-means clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. The proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intra-cluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality.

关键词： image denoising image restoration Bayesian methods Signal to noise ratio Noise reduction Degradation Discrete wavelet transforms PSNR Noise shaping Filtering

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Regularization of Incompletely, Irregularly and Randomly Sampled Data

Regularization of Incompletely, Irregularly and Randomly Sam...

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

作者： C.S. Sastry IT Bhawan GEC Ranjhi PDPM Indian Institute of Information Technology Jabalpur Madhya Pradesh India

In several scientific areas, data are sampled irregularly and insufficiently due to practical and economical limitations. The use of such data in applications results in some artifacts and poor spatial resolution. Therefore, before being used, the data are to be interpolated onto a regular grid. One of the methods achieving this objective is based on the Fourier reconstruction, which deals with the under-determined system of equations. The Stagewise Orthogonal Matching Pursuit (StOMP) is a recently proposed greedy algorithm. Compared to the other recent algorithms like l 1 - minimization techniques, StOMP admits certain promising features such as faster and simpler implementation even in large scale settings. The present work applies StOMP to the Fourier-based interpolation problem for the signals that have sparse Fourier spectra. The basic objective is to verify empirically the performance of the algorithm if, and how far, the measurement coordinates can be shifted from uniform distribution on the continuous interval. Taking kurtosis as a quantifier for the deviation of distribution from being uniform, we show numerically that the measurement coordinates can be significantly shifted from uniform distribution.

关键词： Matching pursuit algorithms Signal processing algorithms Interpolation Coordinate measuring machines Geophysical measurements image reconstruction Equations Large-scale systems Seismic measurements computer vision

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Semantic Event Detection and Classification in Cricket Video Sequence

Semantic Event Detection and Classification in Cricket Video...

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

作者： M.H. Kolekar K. Palaniappan S. Sengupta Department of Computer Science University of Missouri Columbia MO USA Department of Electronics and ECE Indian Institute of Technology Kharagpur West Bengal India

In this paper, we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. The proposed scheme performs a topdown video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify excitement clips into real-time and replay segments. At level-3, we classify these segments into field view and non-field view based on dominant grass color ratio. At level-4a, we classify field view into pitch-view, long-view, and boundary view using motion-mask. At level-4b, we classify non-field view into close-up and crowd using edge density feature. At level-5a, we classify close-ups into the three frequently occurring classes batsman, bowler/fielder, umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes spectator and playerspsila gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as highlight extraction, and video skimming.

关键词： Event detection Video sequences Layout Classification tree analysis TV broadcasting Digital video broadcasting Games computer vision computer graphics image processing

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High Speed and Memory Efficient Parallel Bit Plane Coding Architecture for JPEG2000

High Speed and Memory Efficient Parallel Bit Plane Coding Ar...

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

作者： Tenugu Suman Sumit Kumar Chatterjee Indrajit Chakrabarti Department of Electronics and Electrical Communications Engineering Indian Institute of Technology Kharagpur India

Bit plane coding (BPC) constitutes an important component of the EBCOT Tier-1 block of JPEG2000 encoder. This paper proposes an efficient parallel hardware structure to implement the computation intensive word level bit plane coding algorithm. The proposed architecture computes the context and decision for all bit planes in parallel. The three coding passes are merged for all bit planes in a scan while the samples are coded in sequence. The proposed parallel BPC architecture offers a speed of 31 over the serial BPC architecture. Its memory requirement is independent of the size of the code block. The speed of the proposed architecture has been shown to be significantly faster than an architecture which has been recently reported in literature. The system architecture has been functionally verified by ModelSim and synthesized by TSMC 0.25 mum vtvt CMOS cell libraries.

关键词： image coding Discrete wavelet transforms computer architecture Concurrent computing Transform coding Parallel architectures Arithmetic Streaming media IEC standards ISO standards

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A Scalable Projective Bundle Adjustment Algorithm using the L infinity Norm

A Scalable Projective Bundle Adjustment Algorithm using the ...

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

作者： Kaushik Mitra Rama Chellappa Department of Electrical and Computer Engineering University of Maryland College Park MD USA

The traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m+n) 3 ) per iteration and memory requirement of O(mn(m+n)), where m is the number of cameras and n is the number of structure points. The sparse version of bundle adjustment has a computational complexity of O(m 3 +mn) per iteration and memory requirement of O(mn). Here we propose an algorithm that has a computational complexity of O(mn(radicm+radicn)) per iteration and memory requirement of O(max(m,n)). The proposed algorithm is based on minimizing the L infin norm of reprojection error. It alternately estimates the camera and structure parameters, thus reducing the potentially large scale optimization problem to many small scale subproblems each of which is a quasi-convex optimization problem and hence can be solved globally. Experiments using synthetic and real data show that the proposed algorithm gives good performance in terms of minimizing the reprojection error and also has a good convergence rate.

关键词： H infinity control Cameras Computational complexity Cost function Convergence State estimation Collaborative work Parameter estimation computer vision computer graphics

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Robust Detection of Microaneurysms for Sight Threatening Retinopathy Screening

Robust Detection of Microaneurysms for Sight Threatening Ret...

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

作者： Abhir Bhalerao Amiya Patanaik Sarabjot Anand Pounnusamy Saravanan Department of Computer Science University of Warwick UK Indian Institute of Technology Kharagpur India Department of Diabetes and Endocrinology University Hospitals Coventry and Warwickshire UK

Diabetic retinopathy is one of the major causes of blindness. However, diabetic retinopathy does not usually causea loss of sight until it has reached an advanced stage. The earliest sign of the disease are microaneurysms (MA) which appear as small red dots on retinal fundus images. Various screening programmes have been established in the UK and other countries to collect and assess images on a regular basis, especially in the diabetic population. A considerable amount of time and money is spent in manually grading these images, a large percentage of which are normal. By automatically identifying the normal images, the manual workload and costs could be reduced greatly while increasing the effectiveness of the screening programmes. A novel method of microaneurysm detection from digital retinal screening images is proposed. It is based on filtering using complex-valued circular-symmetric filters, and an eigen-image, morphological analysis of the candidate regions to reduce the false-positve rate. We detail the image processing algorithms and present results on a typical set of 89 image from a published database. Our method is shown to have a best operating sensitivity of 82.6% at a specificity of 80.2% which makes it viable for screening. We discuss the results in the context of a model of visual search and the ROC curves that it can predict.

关键词： Robustness Retinopathy Diabetes Retina Blindness Diseases Costs Filtering Filters 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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