Fast object recognition by parallel image matching on a distributed system

Image matching based on image feature pixels involves heavily iterated computation and frequent memory access. The key to increase the speed is to employ parallelism on either parallel machines or workstation clusters. This paper presents the development of a parallel image matching system which uses a divide-and-conquer method to implement the proposed hierarchical matching scheme on a networked workstation cluster. Our investigation shows that a distributed workstation cluster can best meet the demand of high computation and memory access in image processing. The performance of our proposed matching scheme is evaluated in terms of execution time.

[1]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

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

[3]  John R. Corbin.  The Art of Distributed Applications , 1991, Sun Technical Reference Library.

[4]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[5]  Krzysztof Zielinski,et al.  Parallel programming systems for LAN distributed computing , 1994, 14th International Conference on Distributed Computing Systems.

[6]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[7]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  Henri E. Bal.  Programming distributed systems , 1990 .

[9]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Andrzej M. Goscinski,et al.  Distributed operating systems - the logical design , 1991 .

[11]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Xiaodong Zhang,et al.  Distributed image edge detection methods and performance , 1994, Proceedings of 1994 6th IEEE Symposium on Parallel and Distributed Processing.

[13]  E. Pissaloux,et al.  A guided image matching approach using Hausdorff distance with interesting points detection , 1994, Proceedings of 1st International Conference on Image Processing.