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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008"

共 102 条记录，以下是41-50 订阅

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Multi-Resolution Tracking in Space and Time

Multi-Resolution Tracking in Space and Time

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6th indian conference on computer vision, graphics and image processing

作者： Roy, Sumantra Dutta Tran, Son Dinh Davis, Larry Steven Vikram, B. Sreenivasa Indian Inst Technol Delhi Dept Elect Engn Delhi India Univ Maryland Dept Comp Sci College Pk MD USA Indian Inst Technol Bharti Sch Telecom Tech & Management Delhi India

ISBN: (纸本)9781424442195

this paper proposes efficient and robust methods for tracking a moving object at multiple spatial and temporal resolution levels. the efficiency comes from optimising the amounts of spatial and temporal data processed. the robustness results from multi-level coarse-to-fine state-space searching. Tracking across resolution levels incurs a accuracy-versus-speed trade-off. For example, tracking at higher resolutions incurs greater processing cost, while maintaining higher accuracy in estimating the position of the moving object. We propose a novel spatial multi-scale tracker that tracks at the optimal accuracy-versus-speed operating point. Next, we relax this requirement to propose a multi-resolution tracker that operates at a minimum acceptable performance level. Finally, we extend these ideas to a multi-resolution spatio-temporal tracker We show results of extensive experimentation in support of the proposed approaches.

关键词： Economic and social effects

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Regularization of incompletely, irregularly and randomly sampled data

Regularization of incompletely, irregularly and randomly sam...

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6th indian conference on computer vision, graphics and image processing

作者： Sastry, C. S. PDPM Indian Inst Info Tech Design & Mfg GEC IT Bhawan Jabalpur 482011 MP India

ISBN: (纸本)9781424442195

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： Fourier transforms

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Object Category Detection by Statistical Test of Hypothesis

Object Category Detection by Statistical Test of Hypothesis

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6th indian conference on computer vision, graphics and image processing

作者： Sharma, Gaurav Chaudhury, Santanu Srivastava, J. B. Indian Inst Technol Delhi Dept Math Delhi India Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781424442195

We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. the problem is cast in a statistical hypothesis testing framework. the image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, back-ground, illumination, viewpoint and occlusions.

关键词： Statistical tests

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An Evolutionary Based Slow and Fast Moving Video Object Detection Scheme Using Compound Markov Random Field Model

An Evolutionary Based Slow and Fast Moving Video Object Dete...

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6th indian conference on computer vision, graphics and image processing

作者： Subudhi, Badri Narayan Nanda, Pradipta Kumar Natl Inst Technol Dept Elect Engn Rourkela India CV Raman Coll Engn Dept Elect & TeleCommun Bhubaneswar Orissa India

ISBN: (纸本)9781424442195

We propose an evolving scheme to detect slow as well as fast moving objects in a video sequence. the proposed scheme employ, both spatio-temporal and temporal segmentation to obtain the Video Object plane and hence detection. We propose a Compound Markov Random Field Model as the a priori image model that takes into account the spatial distribution of the current frame, temporal frames and the edge maps of the temporal frames. the spatio-temporal segmentation is cast as a pixel labeling problem and the labels are the MAP estimates. the MAP estimates of a frame are obtained by a hybrid algorithm. the spatial segmentation of a given frame evolves to generate the spatial segmentation of the subsequent frames. the evolved spatial segmentation together with the temporal segmentation produces the Video Object Plane (VOP) and hence detection. Our scheme does require the computation of spatio-temporal segmentation of the initial frame thus speeding up the whole process. the results of the proposed scheme are compared with JSEG method are found to be better in terms of the misclassification error

关键词： image segmentation

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Object Mining using a Matching Graph on Very Large image Collections

Object Mining using a Matching Graph on Very Large Image Col...

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6th indian conference on computer vision, graphics and image processing

作者： Philbin, James Zisserman, Andrew Univ Oxford Dept Engn Sci Visual Geometry Grp Oxford OX1 2JD England

ISBN: (纸本)9781424442195

Automatic organization of large, unordered image collections is an extremely challenging problem with many potential applications. Often, what is required is that images taken in the same place, of the same thing, or of the same person be conceptually grouped together. this work focuses on grouping images containing the same object, despite significant changes in scale, viewpoint and partial occlusions, in very large (1M+) image collections automatically gathered from Flickr the scale of the data and the extreme variation in imaging conditions makes the problem very challenging. We describe a scalable method that first computes a matching graph over all the images. image groups can then be mined from this graph using standard clustering techniques. the novelty we bring is that both the matching graph and the clustering methods are able to use the spatial consistency between the images arising from the common object (if there is one). We demonstrate our methods on a publicly available dataset of 5K images of Oxford, a 37K image dataset containing images of the Statue of Liberty and a much larger 1M image dataset of Rome. this is, to our knowledge, the largest dataset to which image-based data mining has been applied.

关键词： Data mining

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Lossless compression of digital mammography using fixed block segmentation and pixel grouping

Lossless compression of digital mammography using fixed bloc...

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6th indian conference on computer vision, graphics and image processing, icvgip 2008

作者： Kumar, Ravi Koliwad, Shivaprakash Dwarakish, G.S. Department of IS and E. KVGCE Sullia Karnataka India Department of E and C. MCE Hassan Karnataka India Department of applied mechanics and hydraulics NITK Suratkal Karnataka India

ISBN: (纸本)9780769534763

A mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. this is an efficient means of early detection of breast cancer. High resolution is a common characteristic of such images. Archiving and retaining these data for at least three years is expensive, difficult and requires sophisticated data compression techniques. In this paper an efficient method is proposed for lossless compression of mammography images. After performing de-correlation of the image using two efficient predictors, the residue image is divided into 4x4 blocks. the blocks with all-zero pixels are identified using one bit code. Later, Second order of pixel grouping is employed to the remaining blocks to increase the coding efficiency. Such blocks are coded using Base offset method. Special techniques are used to save the header information. the method is tested using 25 mammograms from the MIAS database, each having a resolution of 1024x1024 pixels with 8 bits/pixel. Experimental results indicate better compression ratio when compared to JPEG 2000, JPEG-LS, PNG and JBIG. © 2008 IEEE.

关键词： image compression

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Improving image decomposition method of the 3-MRC coding of scanned compound document images

Improving image decomposition method of the 3-MRC coding of ...

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6th indian conference on computer vision, graphics and image processing

作者： Lakhani, Gopal Texas Tech Univ Lubbock TX 79424 USA

ISBN: (纸本)9781424442195

this paper discusses image decomposition problem of the 3-layer MRC model based coding of scanned (noisy) document images. A widely-used approach for document decomposition is to divide the document image into blocks and split the pixel histogram of each block into two halves by minimizing the sum of variance of its pixels with the mean of the halms. We propose to split a block by minimizing the variance of one half with its minimum pixel and the variance of the other half with its maximum pixel. Our goal is to increase the gap between the two halves by avoiding splitting of any cluster of pixels into both halves. It should help reduce complexity of the generated mask. Moreover, we do not decompose a block if it has no edge points, again to reduce the mask complexity. We also implement a noise reduction heuristic in the mask layer to correct placement of transition pixels. We provide simple analysis and evaluate block energy in terms of the DCT coefficients of the resulting FG/BG layer blocks. Experimental results show that code size of the mask layer of our test images, obtained using proposed processing is reduced to nearly half of the mask obtained by a straightforward 3-MRC implementation.

关键词： Document image coding Mixed raster content model mask noise reduction

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Quantization Based Data Hiding Scheme for Efficient Quality Access Control of images using DWT via Lifting

Quantization Based Data Hiding Scheme for Efficient Quality ...

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6th indian conference on computer vision, graphics and image processing

作者： Phadikar, Amit Maity, Santi P. Kundu, Malay K. MCKV Inst Engn Dept Informat Technol Liluah 711204 Howrah India Bengal Engn & Sci Univ Dept Informat Technol Howrah 711103 India Indian Stat Inst Mach Intelligence Unit & Ctr Soft Comp Kolkata 700108 India

ISBN: (纸本)9781424442195

this paper proposes a transform domain data-hiding scheme for quality access control of images. the original image is decomposed into tiles by applying n-level lifting-based Discrete Wavelet Transformation (DWT). A binary watermark image (external information) is spatially dispersed using the sequence of number generated by a secret key. the encoded watermark bits are then embedded into all DWT-coefficients of n(th)-level and only in the high-high (HH) coefficients of the subsequent levels using dither modulation (DM) but without complete self-noise suppression. It is well known that due to insertion of external information, there will be degradation in visual quality of the host image (cover). the degree of deterioration depends on the amount of external data insertion as well as step size used for DM. If this insertion process is reverted, better quality of images can be accessed. To achieve that goal, watermark bits are detected using minimum distance decoder and the remaining self-noise due to information embedding is suppressed to provide better quality of image. the simulation results have shown the validity of this claim.

关键词： Quality control

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Integrated Detect-Track Framework for Multi-view Face Detection in Video

Integrated Detect-Track Framework for Multi-view Face Detect...

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6th indian conference on computer vision, graphics and image processing

作者： Anoop, K. R. Anandathirtha, Paresh Ramakrishnan, K. R. Kankanhalli, Mohan S. Indian Inst Sci Dept Elect Engn Bangalore 560012 Karnataka India Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9781424442195

An Experiential sampling and Meanshift tracker based Multi-view face detection in video is proposed in this paper In this framework, instead of performing face detection at every position in a frame, we determine certain key positions to run the multiview face detectors. these key positions are statistical samples drawn from a density function that is estimated based on color cues, past detection results, Meanshift tracker results and a temporal continuity model. these samples are then propogated using a Particle filter framework. We use a Meanshift tracker to track faces that are missed by the multiview face detectors. Our framework results in a significant reduction in computation time and accounts for the detection of complete 180 degree pose of the face. We also come up with a novel likelihood measure for track termination, which becomes important when used for detection purposes.

关键词： Face recognition

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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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6th indian conference on computer vision, graphics and image processing

作者： Tiwari, Anil Kumar Kumar, R. V. Raja LNM Inst Informat Technol Jaipur Rajasthan India Indian Inst Technol Kharagpur W Bengal India

ISBN: (纸本)9781424442195

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： Pixels

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