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

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Visibility Cuts: A System for Rendering Dynamic Virtual Environments

Visibility Cuts: A System for Rendering Dynamic Virtual Envi...

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

作者： Soumyajit Deb Ankit Gupta Microsoft Research India India University of Washington USA

The subject of occlusion culling of large 3D environments has received substantial contribution. However the major amount of research into the area has focussed on occlusion culling of static scenes using spatial partitioning. The primary aim of all these schemes is to minimize the load on the GPU by reducing the number of primitives to be rendered. We present an efficient algorithm for visibility culling that supports static and dynamic scenes with equal ease with significant performance improvements over existing schemes. For a given camera position the status of the object nodes in an object hierarchy can be seen as a visibility cut, the nodes of which are either outside the view frustum, or hidden or visible. We propose an efficient update scheme of this visibility cut while processing each frame, taking full advantage of the object hierarchy with spatial and temporal coherency. The whole scene walk through is modelled as a discrete event simulation where every change generates an event scheduled for that particular frame. For occlusion culling, we employ occlusion queries which helps the system to be output sensitive. The system supports transparency of entities without a major performance hit. We propose a scheme to select the level of detail of an object based on the results of occlusion queries.

关键词： Virtual environment Rendering (computer graphics) Layout computer graphics Tree graphs Application software computer vision Hardware image processing Partitioning algorithms

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

作者： Himanshu Arora Anoop M. Namboodiri Center for Visual Information Technology Indian Institute of Information Technology Hyderabad India

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 any 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： Strontium image resolution image generation computer vision image processing image reconstruction Inference algorithms Layout computer graphics Information technology

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

作者： Andrew Janowczyk Sharat Chandran Srinivas Aluru ViGIL Department of Computer Science and Engineering Indian Institute of Technology Bombay India Iowa State University USA

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) 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 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 not withstanding, 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 speed up 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： Random number generation Principal component analysis Parallel algorithms computer vision Application software Parallel processing computer graphics image processing computer science Hardware

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

作者： Kota Naga Srinivasarao Batta Indrajit Chakrabarti Dept. of Electron. & Electr. Commun. Eng. Indian Inst. of Technol. Kharagpur Department of Electronics and Electrical Communications Engineering Indian Institute of Technology Kharagpur India

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： Parallel architectures Motion estimation computer architecture Video compression Hardware computer vision computer graphics image processing Underwater communication Paper technology

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image Denoising Using Matched Biorthogonal Wavelets

Image Denoising Using Matched Biorthogonal Wavelets

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

作者： Sanjeev Pragada Jayanthi Sivaswamy Center for Visual Information Technology International Institute of Information Technology Hyderabad India

Current denoising techniques use the classical ortho normal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. The use of available biorthogonal wavelets in image denoising is less common because of their poor performance. In this paper, we present a method to design image-matched biorthogonal wavelet bases and report on their potential for denoising. We have conducted experiments on various image datasets namely Natural, Satellite and Medical with the designed wavelets using two existing thresholding strategies. Test results from comparing the performance of matched and fixed biorthogonal wavelets show an average improvement of 35% in MSE for low SNR values (0 to 18 db) in every dataset. This improvement was also seen in the PSNR and visual comparisons. This points to the importance of matching when using wavelet-based denoising.

关键词： image denoising Noise reduction Signal design Additive white noise Information technology Design methodology image coding Gaussian noise Kernel Finite impulse response filter

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Multi-resolution Tracking in Space and Time

Multi-resolution Tracking in Space and Time

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

作者： Sumantra Dutta Roy Son Dinh Tran Larry Steven Davis B. Sreenivasa Vikram Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Computer Science University of Maryland College Park USA Bharti School of Telecom Tech & Management Indian Institute of Technology Delhi India

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 an 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： Spatial resolution Bayesian methods Filtering computer vision Space technology computer science Educational institutions Robustness Costs Spatiotemporal phenomena

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Real-Time Rendering and Manipulation of Large Terrains

Real-Time Rendering and Manipulation of Large Terrains

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

作者： Shiben Bhattacharjee Suryakant Patidar P.J. Narayanan Center for Visual Information Technology International Institute of Information Technology Hyderabad India

Terrains are challenging geometric objects for real-time rendering and interactive manipulation. State-of-the-art terrain rendering systems use custom, multi-resolution, representations like geometry clipmaps for fast rendering on the GPU. In this paper, we present a system that exploits the power and flexibility of the modern GPUs to store, render, and manipulate terrains with minimal CPU involvement. The central idea is to use a regular-grid representation and fixed size blocks/tiles that change in resolution. The potentially visible portion of the terrain is cached at the highest necessary resolution and is rendered from the GPU. The CPU sends a light geometry template which is expanded by the Geometry Shader to the triangles, using the heights stored in the GPU Cache. The CPU performs a coarse culling of the tiles with the GPU performing fine culling. The GPU cache is updated continuously as the viewpoint changes. Our system enables the terrain to be modified procedurally or edited interactively on the GPU with no CPU involvement. The terrain can also interact with a large number of external objects in real-time entirely within the GPU. We achieve a consistent rendering rate of 100 frames per second with terrain modification and interactions as well as a triangle rate of upto 350 million per second on an Nvidia 8800 GTX GPU for large terrains, with a CPU load below 10%.

关键词： Geometry Rendering (computer graphics) Tiles Information technology Physics computer vision computer graphics image processing Aerospace simulation Geographic Information Systems

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Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

Learning Feature Trajectories Using Gabor Filter Bank for Hu...

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

作者： Sunil Kumar Gupta Y. Senthil Kumar K.R. Ramakrishnan Indian Institute of Science Bangalore India

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

关键词： Gabor filters Humans Surveillance Videos Clustering algorithms computer vision image segmentation Application software Legged locomotion Hidden Markov models

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

作者： Badri Narayan Subudhi Pradipta Kumar Nanda Department of Electrical Engineering National Institute of Technology Rourkela India Department of Electonics and Telecommunication C. V. Raman College of Engineering Bhubaneswar India

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： Object detection Markov random fields image segmentation Video sequences Context modeling computer vision Stochastic processes Genetic algorithms Dissolved gas analysis computer graphics

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VLSI Implementation of Fast Connected Component Labeling Using Finite State Machine Based Cell Network

VLSI Implementation of Fast Connected Component Labeling Usi...

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

作者： Pradipta Roy Prabir Kumar Biswas Optronics Centre Integrated Test Range Chandipur India Department of E&ECE IIT Kharagpur India

Connected component labeling of a binary image is an indispensable task for image segmentation and analysis. For real time video object segmentation, total processing time to label all the objects of an entire image is critical as it is constrained by inter frame temporal difference. Parallel dedicated hardware is necessary to solve this problem in real time. In this paper we have proposed a parallel VLSI architecture for fast connected component labeling of a binary video frame image. We have adopted a seeded region-growing algorithm, which is implemented in a state machine based cell network. The design is verified in XILINX FPGA with real time video image data containing different objects with different shapes and sizes. The worst-case labeling time for a full video frame is 5 ms (using a 32 MHz clock), which is well below the required inter frame timing interval of 40 ms.

关键词： Very large scale integration Labeling Automata image segmentation image analysis Object segmentation Hardware Field programmable gate arrays Shape Clocks

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