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International Conference on Pattern Recognition

作者： Keun-Chung Kim Doo-Young Kim J.K. Aggarwal Department of Computer Science YangSan College YangSan South Korea Department of Electronics Dong-A University busan South Korea Computer and Vision Research Center University of Technology Austin TX USA

The process of edge detection and feature extraction methods is based on converting a change of gray level between two regions of an image into a variation function that gives the difference between the gray level of each region and the gray level of the line of discontinuity. The process of computing the relative difference yields the magnitude, direction, and slope sign of the magnitude, which in turn characterize the features of an edge. By focusing on the relative variation of gray level for pixels between small regions, the filtering process of the local region identifies and smoothes uncorrelated features for a truer edge than is possible by smoothing a larger region.

关键词： Feature extraction Gray-scale Image edge detection Reactive power Displays computer science computer vision Educational institutions Image converters Smoothing methods

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Preface

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International Journal of Pattern Recognition and Artificial Intelligence 1998年第1期12卷 1-4页

作者： Seong-Whan Lee Yuan Y. Tang Patrick S. P. Wang Center for Artificial Vision Research Korea University Anam-dong Seongbuk-ku Seoul 136-701 Korea Department of Computing Studies Hong Kong Baptist University Kowloon-Tong Hong Kong P.R.China College of Computer Science Northeastern University Boston MA 02115 USA

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Toward motion picture grammars 3rd

Toward motion picture grammars

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3rd Asian Conference on computer vision, ACCV 1998

作者： Bolle, Ruud Aloimonos, Yiannis Fermüller, Cornelia Exploratory Computer Vision Group IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States Computer Vision Laboratory Center for Automation Research Institute for Advanced Computer Studies Computer Science Department University of Maryland College ParkMD20742-3275 United States

ISBN: (纸本)3540639314

We are interested in processing video data for the purpose of solving a variety of problems in video search, analysis, indexing, browsing and compression. Instead of concentrating on a particular problem, in this paper we present a framework for developing video applications. Our basic thesis is that video data can be represented at a higher level of abstraction as a string generated by a grammar, termed motion picture grammar. The rules of that grammar relate different spatiotemporal representations of the video content and, in particular, representations of action. © 1997, Springer Verlag. All rights reserved.

关键词： Motion pictures

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The confounding of translation and rotation in reconstruction from multiple views

The confounding of translation and rotation in reconstructio...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： C. Fermuller Y. Aloimonos Computer Vision Laboratory Center for Automation Research Institute for Advanced Computer Studies and the Computer Science Department University of Maryland College Park MD USA

If 3D rigid motion is estimated with some error a distorted version of the scene structure will in turn be computed. Of computational interest are these regions in space where the distortions are such that the depths become negative, because in order to be visible the scene has to lie in front of the image. The stability analysis for the structure-from-motion problem presented in this paper investigates the optimal relationship between the errors in the estimated translational and rotational parameters of a rigid motion, that results in the estimation of a minimum number of negative depth values. The input used is the value of the flow along some direction, which is more general than optic flow or correspondence. For a planar retina it is shown that the optimal configuration is achieved when the projections of the translational and rotational errors on the image plane are perpendicular. Furthermore, the projection of the actual and the estimated translation lie on a line passing through the image center. For a spherical retina given a rotational error, the optimal translation is the correct one, while given a translational error. The optimal rotational error is normal to the translational one at an equal distance from the real and estimated translations. The proofs, besides illuminating the confounding of translation and rotation in structure from motion, have an important application to ecological optics, explaining differences of planar and spherical eye or camera designs in motion and shape estimation.

关键词： Motion estimation Layout Image motion analysis Optical distortion Retina Error correction Image reconstruction Stability analysis Motion analysis Cameras

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Efficient Window Block Retrieval in Quadtree-Based Spatial Databases

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GeoInformatica 1997年第1期1.0卷 59-91页

作者： Aref, Walid G. Samet, Hanan Computer Science Department Center for Automation Research University of Maryland College Park MD 20742 United States University of Alexandria Egypt University of Maryland College Park United States Matsushita Info. Technol. Laboratory Princeton United States IBM Research Almaden CA United States University of Maryland Inst. for Advanced Computer Studies College Park MD United States ACM IEEE United States Department of Computer Science University of Maryland United States Computer Vision Laboratory Stanford University United States ACM IEEE Intl. Assoc. of Pattern Recognition

An algorithm is presented to answer window queries in a quadtree-based spatial database environment by retrieving all of the quadtree blocks in the underlying spatial database that cover the quadtree blocks that comprise the window. It works by decomposing the window operation into sub-operations over smaller window partitions. These partitions are the quadtree blocks corresponding to the window. Although a block b in the underlying spatial database may cover several of the smaller window partitions, b is only retrieved once rather than multiple times. This is achieved by using an auxiliary main memory data structure called the active border which requires O(n) additional storage for a window query of size n × n. As a result, the algorithm generates an optimal number of disk I/O requests to answer a window query (i.e., one request per covering quadtree block). A proof of correctness and an analysis of the algorithm's execution time and space requirements are given, as are some experimental results.

关键词： Active border Clipping Data structures Databases Design of algorithms Quadtree space decomposition Range query Spatial databases Window block retrieval

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FPGA-based computing in computer vision

FPGA-based computing in computer vision

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computer Architectures for Machine Perception (CAMP)

作者： N.K. Ratha A.K. Jain Exploratory Computer Vision Group IBM Thomas J. Watson Research Center Yorktown Heights NY USA Department of Computer Science Michigan State University East Lansing MI USA

Algorithms in computer vision are characterized by (i) complex and repetitive operations; (ii) large amount of data and (iii) a variety of data interaction (e.g., point operations, neighborhood operations, global operations). Based on the computation and communication complexity, vision algorithms have been characterized into three categories: (i) low-level, (ii) intermediate-level and (iii) high-level. In this paper, we describe the usage of custom computing approach to meet the computation and communication needs of computer vision algorithms. By customizing hardware architecture for every application at the instruction level, the optimal grain size needed for the problem at hand and the instruction granularity can be matched. Field Programmable Gate Array (FPGA) based processing elements (PEs) are being used to provide this facility. Using programmable communication resources, the diverse communication requirements can be met. A vision system needs to integrate hardware for the three levels. A custom computing approach alleviates the problem of achieving optimal granularity for different stages as the same hardware gets reconfigured at a software level for different levels of the application. We demonstrate the advantages of our approach using Splash 2-a Xilinx 4010-based custom computer.

关键词： computer vision Image edge detection Layout Machine vision Cameras Detectors Image segmentation Application software Hardware Surface fitting

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Multilayer perceptrons on Splash 2

Multilayer perceptrons on Splash 2

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computer Architectures for Machine Perception (CAMP)

Multilayer perceptrons (MLPs) are one of the most popular neural network models for solving pattern classification and image classification problems. Because of their ability to learn complex decision boundaries, MLPs are used in many practical computer vision applications involving classification (or supervised segmentation). Once the connection weights in a MLP have been learnt, the network can be used repeatedly for classification of new input patterns. Several special-purpose architectures have been described in the literature for neural networks as they are slow on a conventional uniprocessor. In this paper, we describe mapping of MLPs onto Splash 2-a "custom computing machine". The main features of the proposed mapping are: (i) the number of nodes in a layer is not fixed; (ii) the number of layers in the network is not fixed; (iii) it is based on a set of reprogrammable FPGAs and a programmable crossbar; and (iv) it has a significant speedup over a uniprocessor. The mapping has been used for implementing a 3-layer MLP for page segmentation application with an appreciable speedup of approximately 150 over a SPARCstation 20 for one million pattern vectors with 20 features per pattern.

关键词： Multilayer perceptrons Neural networks Multi-layer neural network Pattern classification Image classification computer vision Application software Image segmentation computer architecture Field programmable gate arrays

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Learning parameterized models of image motion

Learning parameterized models of image motion

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M.J. Black Y. Yacoob A.D. Jepson D.J. Fleet Xerox Palo Alto Research Center Palo Alto CA USA Computer Vision Laboratory University of Maryland College Park MD USA Department of Computer Science University of Toronto Toronto ONT Canada Department of Computing and Information Science Queen's University Kingston ONT Canada

A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.

关键词： Image motion analysis Motion estimation Humans Optical computing Image recognition Deformable models Robustness Mouth Face recognition computer vision

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The area bisectors of a polygon and force equilibria in programmable vector fields 97

The area bisectors of a polygon and force equilibria in prog...

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Proceedings of the thirteenth annual symposium on Computational geometry

作者： Karl-Friedrich Böhringer Bruce Randall Donald Dan Halperin ALPHA Lab Dept. of Ind. Eng. and Op. Research Univ. of CA Berkeley and Robotics & Vision Lab Dept. of Comp. Sc. Cornell Univ. Robotics & Vision Laboratory Department of Computer Science Cornell University Dept. of Comp. Sc. Tel Aviv Univ. Tel Aviv 69978 Israel

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Recognizing objects using scale space local invariants

Recognizing objects using scale space local invariants

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Bruckstein, A.M. Rivlin, E. Weiss, I. Department of Computer Science Israel Institute of Technology Technion Haifa Israel Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD 20742-3275 United States

ISBN: (纸本)081867282X

In this paper we discuss a new approach to invariant signatures for recognizing curves under viewing distortions and partial occlusion. The approach is intended to overcome the ill-posed problem of finding derivatives, on which local invariants usually depend. The basic idea is to use invariant finite differences, with a scale parameter that determines the size of the differencing interval. The scale parameter is allowed to vary so that a "scale space"-like invariant representation of the curve, with larger difference intervals corresponding to larger coarser scales, can be obtained. In this new representation, each traditional local invariant is replaced by a scale-dependent range of invariants. Thus, instead of invariant signature curves we obtain invariant signature surfaces in a 3D invariant "scale space". © 1996 IEEE.

关键词： Pattern recognition

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