Parallel Image Processing System on a Cluster of Personal Computers (Best Student Paper Award: First Prize)

The most demanding image processing applications require real time processing, often using special purpose hardware. The work herein presentedrefers to the application of cluster computing for off line image processing, where the end user benefits from the operation of otherwise idle processors in the local LAN. The virtual parallel computer is composed by off-the-shelf personal computers connected by a low cost network, such as a 10 Mbits/s Ethernet. The aim is to minimise the processing time of a high level image processing package. The system developed to manage the parallel execution is describedand some results obtained for the parallelisation of high level image processing algorithms are discussed, namely for active contour and modal analysis methods which require the computation of the eigenvectors of a symmetric matrix.

[1]  James Demmel,et al.  Applied Numerical Linear Algebra , 1997 .

[2]  Jorge G. Barbosa,et al.  Linear algebra algorithms in a heterogeneous cluster of personal computers , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[3]  Leslie G. Valiant,et al.  A bridging model for parallel computation , 1990, CACM.

[4]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[5]  David E. Culler,et al.  A case for NOW (networks of workstation) , 1995, PODC '95.

[6]  Jack Dongarra,et al.  The design library: of a parallel dense linear algebra software Reduction to Hessenberg, tridiagonal, and bidiagonal form* , 1995 .

[7]  Jorge G. Barbosa,et al.  Algorithm-Dependant Method to Determine the Optimal Number of Computers in Parallel Virtual Machines , 1998, VECPAR.

[8]  Robert A. van de Geijn,et al.  SUMMA: scalable universal matrix multiplication algorithm , 1995, Concurr. Pract. Exp..

[9]  Anil K. Jain,et al.  Parallel implementation of vision algorithms on workstation clusters , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[10]  Jack J. Dongarra,et al.  Key Concepts for Parallel Out-of-Core LU Factorization , 1996, Parallel Comput..

[11]  Jaeyoung Choi,et al.  Design and Implementation of the ScaLAPACK LU, QR, and Cholesky Factorization Routines , 1994, Sci. Program..

[12]  Maximilian Lueckenhaus,et al.  Thread concept for automatic task parallelization in image analysis , 1998, Optics & Photonics.

[13]  Jack Dongarra,et al.  The design of linear algebra libraries for high performance computers , 1993 .

[14]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[15]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[16]  Jun Shen,et al.  An optimal linear operator for step edge detection , 1992, CVGIP Graph. Model. Image Process..

[17]  Stephen M. Smith,et al.  ASSET-2: real-time motion segmentation and shape tracking , 1995, Proceedings of IEEE International Conference on Computer Vision.

[18]  Howard Jay Siegel,et al.  PASM: A Partitionable SIMD/MIMD System for Image Processing and Pattern Recognition , 1981, IEEE Transactions on Computers.

[19]  Kwan Woo Ryu,et al.  The Block Distributed Memory Model , 1996, IEEE Trans. Parallel Distributed Syst..

[20]  R. V. D. Geijn,et al.  LAPACK Working Note 96: Scalable Universal Matrix Multiplication Algorithm , 1995 .

[21]  Larry S. Davis,et al.  Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study , 1996, Proceedings of International Conference on Parallel Processing.

[22]  Chee Sun Won,et al.  A parallel image segmentation algorithm using relaxation with varying neighborhoods and its mapping to array processors , 1987, Computer Vision Graphics and Image Processing.

[23]  Patrick J. Flynn 3-D Object Recognition with Symmetric Models: Symmetry Extraction and Encoding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Michael Brady,et al.  Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..

[25]  Alexandre Alves,et al.  WPVM: parallel computing for the people , 1995, HPCN Europe.

[26]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Robert A. van de Geijn,et al.  Efficient Matrix Inversion via Gauss-Jordan Elimination and ItsParallelization , 1998 .

[28]  Ramesh Subramonian,et al.  LogP: towards a realistic model of parallel computation , 1993, PPOPP '93.

[29]  Jack Dongarra,et al.  LAPACK Working Note 58: ``The Design of Linear Algebra Libraries for High Performance Computers , 1993 .

[30]  Charles L. Seitz,et al.  Myrinet: A Gigabit-per-Second Local Area Network , 1995, IEEE Micro.

[31]  Danny Crookes,et al.  A PVM Implementation of a Portable Parallel Image Processing Library , 1996, PVM.