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CVGIP-GRAPHICAL MODELS AND image processing 1994年第3期56卷 231-246页

作者： EU, D TOUSSAINT, GT Mcgill Univ Sch Comp Sci Montreal H3A 2A7 Quebec Canada

Given a polygonal curve P = [p1, p2, ..., p(n)], the polygonal approximation problem considered calls for determining a new curve P' = [p1', p2', ..., p(m)'] such that (i) m is significantly smaller than n, (ii) the vertices of P' are an ordered subset of the vertices of P, and (iii) any line segment [p(A)', p(A+1)'] of P' that substitutes a chain [p(B), ..., p(C)] in P is such that for all i where B less-than-or-equal-to i less-than-or-equal-to C, the approximation error of p(i) with respect to [p(A)', p(A+1)'], according to some specified criterion and metric, is less than a predetermined error tolerance. Using the parallel-strip error criterion, we study the following problems for a curve P in R(d), where d = 2, 3: (i) minimize m for a given error tolerance and (ii) given m, find the curve P' that has the minimum approximation error over all curves that have at most m vertices. These problems are called the min-# and min-epsilon problems, respectively. For R2 and with any one of the L1, L2, or L(infinity) distance metrics, we give algorithms to solve the min-# problem in O(n2) time and the min-epsilon problem in O(n2 log n) time, improving the best known algorithms to date by a factor of log n. When P is a polygonal curve in R3 that is strictly monotone with respect to one of the three axes, we show that if the L1 and L(infinity) metrics are used then the min-# problem can be solved in O(n2) time and the min-epsilon problem can be solved in O(n3) time. If distances are computed using the L2 metric then the min-# and min-epsilon problems can be solved in O(n3) and O(n3 log n) time, respectively. All of our algorithms exhibit O(n2) space complexity. Finally, we show that if it is not essential to minimize m, simple modifications of our algorithms afford a reduction by a factor of n for both time and space. (C) 1994 Academic Press, Inc.

关键词： Graphic methods

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Texture analysis for image processing on general-purpose parallel machines

Texture analysis for image processing on general-purpose par...

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International Symposium on parallel Architectures, Algorithms and Networks (ISPAN)

作者： L. Boroczky P. Cremonesi N. Scarabottolo KFKI Research Institute for Measurement and Computing Techniques Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Dipartimento di Elettronica e Informazione Politecnico di Milano Milan Italy

The problem considered in this paper is the definition of an efficient parallel algorithm for texture analysis of an image. The target architectures are distributed-memory general-purpose MIMD parallel machines. The solutions proposed here are based on two different methods, the Statistic Feature Matrix and the Wavelet Decomposition.< >

关键词： image analysis image texture analysis image processing parallel machines Concurrent computing Wavelet analysis Algorithm design and analysis distributed computing Power system interconnection Computer networks

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parallel computation of 3D wavelets

Parallel computation of 3D wavelets

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Scalable High Performance Computing Conference

作者： L.R.C. Suzuki J.R. Reid T.J. Burns G.B. Lamont S.K. Rogers Graduate School of Engineering AFIT/ENG Air Force Institute of Technology Dayton OH USA

The Discrete Wavelet Transform (DWT) is becoming a widely used tool in image processing and other data analysis areas. A non-conventional variation of a spatiotemporal 3D DWT has been developed in order to analyze motion in time-sequential imagery. The computational complexity of this algorithm is /spl Theta/(n/sup 3/), where n is the number of samples in each dimension of the input image sequence. methods are needed to increase the speed of these computations for large data sets. Fortunately, wavelet decomposition is very amenable to parallelization. Coarse-grained parallel versions of this process have been designed and implemented on three different architectures: a distributed network represented by a distributed network of Sun SPARCstation 2 workstations: two Intel hypercubes (an iPSC/2 and an iPSC/860); and a Thinking Machines Corporation CM-5, a massively parallel SPMD. This non-conventional 3D wavelet decomposition is very suitable for coarse-grain implementation on parallel computers with proper load balancing. Close to linear speedup over serial implementations has been achieved using a distributed network. Near-linear speedup was obtained on the hypercubes and the CM-5 for a variety of image-processing applications.< >

关键词： Concurrent computing Discrete wavelet transforms Hypercubes Wavelet analysis image processing Data analysis Spatiotemporal phenomena image analysis image motion analysis Motion analysis

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Communication and computation patterns of large scale image convolutions on parallel architectures

Communication and computation patterns of large scale image ...

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International Symposium on parallel processing

作者： S.G. Dykes Xiaodong Zhang Yan Zhou Haixu Yang High Performance Computing and Software Laboratory University of Texas San Antonio San Antonio TX USA

Segmentation and other image processing operations rely on convolution calculations with heavy computational and memory access demands. The article presents an analysis of a texture segmentation application containing a 96/spl times/96 convolution. Sequential execution required several hours an single processor systems with over 99% of the time spent performing the large convolution. 70% to 75% of execution time is attributable to cache misses within the convolution. We implemented the same application on CM-5, iPSC/860 and PVM distributed memory multicomputers, tailoring the parallel algorithms to each machine's architecture. parallelization significantly reduced execution time, taking 49 seconds on a 512 node CM-5 and 6.5 minutes on a 32 node iPSC/860. The results indicate for large kernel convolutions the size and bandwidth of the fast memory store is more important than processor power or communication overhead.< >

关键词： Concurrent computing Large-scale systems parallel architectures image segmentation image processing Kernel Laboratories Bandwidth Pixel Smoothing methods

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基于逻辑神经网络的图象处理方法

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航空学报 1994年第5期15卷 570-575页

作者：杨国庆葛洪纬南京航空航天大学计算机系

阐述了基于逻辑神经网络的图象平滑、压缩编码方法。这些方法的基本思想是：以逻辑神经网络为窗口，选择对某种图象变换敏感的模式训练窗口。当窗口对图象扫描时，窗口在图象的各位置输出响应，通过对这些响应的简单运算，实现所希望的... 详细信息

阐述了基于逻辑神经网络的图象平滑、压缩编码方法。这些方法的基本思想是：以逻辑神经网络为窗口，选择对某种图象变换敏感的模式训练窗口。当窗口对图象扫描时，窗口在图象的各位置输出响应，通过对这些响应的简单运算，实现所希望的处理。除了理论分析外，文中还给出了各种方法的典型试验结果。

关键词：图象处理逻辑设计神经网络

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Mapping pyramids into 3-D meshes

Mapping pyramids into 3-D meshes

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International Conference on parallel and distributed Systems (ICPADS)

作者： K.L. Chung Y.W. Chen Department of Information Management National Taiwan Institute of Technology Taipei Taiwan

Embedding one parallel architecture into another is very important in the area of parallel processing because parallel architectures can vary widely. Given a pyramid architecture of (4/sup N/-1)/3 nodes and height N, this paper presents a mapping method to embed the pyramid architecture into a 2/sup N-1-k//spl times/2/sup N-1-k//spl times/(4/sup k+1/+2)/3 mesh for 0/spl les/k/spl les/N-1. Our method has dilation max{4/sup k/, 2/sup N-2-k/} and expansion 1+2/(4k+1). When setting k=(N-2)/3, the pyramid can be embedded into a 2/sup (2N-1//3)/spl times/2/sup (2N-1//3)/spl times/[4/sup (N+1//3)+2]/3 mesh, and it has dilation and expansion 1+2/[4/sup (N+1//3)]. This result has can optimal expansion when N is sufficiently large and is superior to the previous mapping methods in terms of the same gauges.

关键词： parallel architectures Tree graphs parallel processing Computer architecture Information management parallel algorithms image processing Concurrent computing Embedded computing Computational geometry

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Coarse-grain parallel genetic algorithms: categorization and new approach

Coarse-grain parallel genetic algorithms: categorization and...

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International Symposium on parallel and distributed processing (IPDPS)

作者： Shyh-Chang Lin W.F. Punch E.D. Goodman Genetic Algorithm Research and Applications Group Case Center and Department of Computer Science Michigan State University USA Dept. of Comput. Sci. Michigan State Univ. East Lansing MI USA

This paper describes a number of different coarse-grain GA's, including various migration strategies and connectivity schemes to address the premature convergence problem. These approaches are evaluated on a graph partitioning problem. Our experiments showed, first, that the sequential GA's used are not as effective as parallel GA's for this graph partition problem. Second, for coarse-grain GA's, the results indicate that using a large number of nodes and exchanging individuals asynchronously among them is very effective. Third, GA's that exchange solutions based on population similarity instead of a fixed connection topology get better results without any degradation in speed. Finally, we propose a new coarse-grained GA architecture, the Injection Island GA (iiGA). The preliminary results of iiGA's show them to be a promising new approach to coarse-grain GA's.< >

关键词： Genetic algorithms Convergence Genetic mutations Adaptive systems Circuit testing Application software Computer science Topology Optimization methods image recognition

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parallel implementation of vision algorithms on workstation clusters

Parallel implementation of vision algorithms on workstation ...

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International Conference on Pattern Recognition

作者： D. Judd N.K. Ratha P.K. McKinley J. Weng A.K. Jain Department of Computer Science Michigan State University East Lansing MI USA

parallel implementations of two computer vision algorithms on distributed cluster platforms are described. The first algorithm is a square-error data clustering method whose parallel implementation is based on the well-known sequential CLUSTER program. The second algorithm is a motion parameter estimation algorithm used to determine correspondence between two images taken of the same scene. Both algorithms have been implemented and tested on cluster platforms using the PVM package. Performance measurements demonstrate that it is possible to attain good performance in terms of execution time and speedup for large-scale problems, provided that adequate memory; swap space, and I/O capacity are available at each node.

关键词： Workstations Clustering algorithms Computer vision Clustering methods Parameter estimation Layout Testing Packaging Measurement Large-scale systems

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EVOLVING NEURAL-NETWORK PATTERN CLASSIFIERS 2

EVOLVING NEURAL-NETWORK PATTERN CLASSIFIERS

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CONF ON NEURAL AND STOCHASTIC methods IN image AND SIGNAL processing 2

作者： MCDONNELL, JR WAAGEN, DE PAGE, WC Naval Command Control and Ocean Surveillance Ctr. (United States)

ISBN: (纸本)0819412813

This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems.

关键词： Computer programming Neural networks Network architectures Stochastic processes Neurons Evolutionary optimization Autoregressive models image classification Brain mapping Chemical elements

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A CONVERGENCE MEASURE AND SOME parallel ASPECTS OF MARKOV-CHAIN MONTE-CARLO ALGORITHMS 2

A CONVERGENCE MEASURE AND SOME PARALLEL ASPECTS OF MARKOV-CH...

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CONF ON NEURAL AND STOCHASTIC methods IN image AND SIGNAL processing 2

作者： MALFAIT, M ROOSE, D VANDERMEULEN, D K.U.Leuven Department of Computer Science Celestijnenlaan 200A Heverlee 3001 Belgium K.U.Leuven Interdisciplinary Research Unit for Radiological Imaging (ESAT + Radiology) Kard. Mercierlaan 94 Heverlee 3001 Belgium

ISBN: (纸本)0819412813

We examine methods to assess the convergence of Markov chain Monte Carlo (MCMC) algorithms and to accelerate their execution via parallel computing. We propose a convergence measure based on the deviations between simultaneously running MCMC algorithms. We also examine the acceleration of MCMC algorithms when independent parallel samplers are used and report on some experiments with coupled samplers. As applications we use small Ising model simulations and a larger medical image processing algorithm. © 1993 SPIE. All rights reserved.

关键词： Ising model

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