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

作者： Suman, Tenugu Chatterjee, Sumit Kumar Chakrabarti, Indrajit Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424442195

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 codeblock. 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 mu m vtvt CMOS cell libraries.

关键词： bit plane coding JPEG2000 encoder codeblock parallel architecture

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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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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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A Parallel Architecture for Successive Elimination Block Matching Algorithm

A Parallel Architecture for Successive Elimination Block Mat...

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

作者： Srinivasarao, B. K. N. Chakrabarti, Indrajit Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424442195

this paper proposes a parallel architecture for a successive elimination algorithm (SEA), which is used in block matching motion estimation. SEA effectively eliminates the search points within the search window and thus decreases the number of matching evaluation instances that require very intensive computations compared to the standard full search algorithm (FSA). the proposed architecture for SEA decreases the time to calculate the motion vector by 57 percent compared to FSA. the performance while applying the SEA to several standard video clips has been shown to be same compared to the standard FSA. the proposed architecture uses 16 processing elements accompanied with use of intelligent data arrangement and memory configuration. A technique for reducing external memory accesses has also been developed. the proposed architecture for SEA provides an efficient solution for applications requiring real-time motion estimations. For, it serves to compute motion vectors in less amount of time while requiring almost same power and some increase in area compared to a similar architecture for implementing the full search algorithm. A register-transfer level implementation as well as simulation results on benchmark video clips are presented. Relevant design statistics on area and power for comparing between SEA and FSA implementations are also provided.

关键词： motion estimation parallel architectures successive elimination video processing processing element

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Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application

Fast, Processor-Cardinality Agnostic PRNG with a Tracking Ap...

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

作者： Janowczyk, Andrew Chandran, Sharat Aluru, Srinivas Indian Inst Technol Dept Comp Sci & Engn ViGIL Bombay 400076 Maharashtra India Iowa State Univ Sci & Technol Ames IA 50011 USA

ISBN: (纸本)9781424442195

As vision algorithms mature with increasing inspiration from the learning community, statistically independent pseudo random number generation (PRNG) becomes increasingly important. At the same time, execution time demands have seen algorithms being implemented on evolving parallel hardware such as GPUs. the Mersenne Twister (MT) [7] has proven to be the current state of the art for generating high quality random numbers, and the Nvidia provided software for parallel MT is in widespread use. While execution time is important, development time is also critical. As processor cardinality changes, a foundation for generating simulations that will vary only in execution time and not in the actual result is useful;otherwise the development time will be impacted. In this paper we present an implementation of the Lagged Fibonacci Generator (LFG) considered to be of quality equal [7] to MT on the GPU. Unlike MT LFG has this important processor-cardinality agnostic capability that is - as the number of processing resources changes, the overall sequence of random numbers remains the same. this feature notwithstanding, our basic implementation is roughly as fast as the parallel MT;an in-memory version is actually 25% faster in execution time. Both parallel MT as well as parallel LFG show enormous speedup over their sequential counterparts. Finally, a prototype particle filter tracking application shows that our method works not just in parallel computing theory, but also in practice for vision applications, providing a decrease of 60% in execution time.

关键词： Program processors

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

作者： Bhalerao, Abhir Patanaik, Amiya Anand, Sarabjot Saravanan, Pounnusamy Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Indian Inst Technol Kharagpur W Bengal India Univ Hosp Coventry & Warwickshire Warwickshire England

ISBN: (纸本)9781424442195

Diabetic retinopathy is one of the major causes of blindness. However diabetic retinopathy does not usually cause a 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-positive 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： Grading

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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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How much zoom is the right zoom from the perspective of Super-Resolution?

How much zoom is the right zoom from the perspective of Supe...

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

作者： Arora, Himanshu Namboodiri, Anoop M. IIIT Ctr Visual Informat Technol Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9781424442195

Constructing a high-resolution (HR) image from low-resolution (LR) image(s) has been a very active research topic recently with focus shifting from multi-frames to learning based single-frame super-resolution (SR). Multi-frame SR algorithms attempt the exact reconstruction of reality, but are limited to small magnification factors. Learning based SR algorithms learn the correspondences between. LR and HR patches. Accurate replacements or revealing the exact underlying information is not guaranteed in many scenarios. In this paper we propose an alternate solution. We propose to capture images at right zoom such that it has just sufficient amount of information so that further resolution enhancements can be easily achieved using an v off the shelf single-frame SR algorithm. this is true under the assumption that such a zoom factor is not very high, which is true for most man-made structures. the low-resolution. image is divided into small patches and ideal resolution is predicted for every patch. the contextual information is incorporated using a Markov Random Field based prior. Training data is generated from high-quality images and can use any single-frame SR algorithm. Several constraints are proposed to minimize the extent of zoom-in. We validate the proposed approach on synthetic data and real world images to show the robustness.

关键词： image enhancement

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

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

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

作者： Nandi, Sudipto Guha, Prithwijit Venkatesh, K. S. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424442195

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 back-ground 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： Clustering algorithms

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

作者： Raghavendra, R. Rao, Ashok Kumar.G, Hemantha Department of studies in Computer Science University of Mysore Mysore-570 006 India Dept of Computer Science J.S.S. College of Arts Commerce and Science Mysore-570 025 India

ISBN: (纸本)9780769534763

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. © 2008 IEEE.

关键词： Principal component analysis

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