Parallel Algorithms for Image Enhancement and Segmentation by Region Growing with an Experimental Study
Parallel Processing Symposium, International (1996)
Apr. 15, 1996 to Apr. 19, 1996
David A. Bader , Institute for Advanced Computer Studies
Joseph JaJa , Institute for Advanced Computer Studies
David Harwood , Institute for Advanced Computer Studies
and Larry S. Davis , Institute for Advanced Computer Studies
This paper presents efficient and portable implementations of a useful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique which makes use of the SNF and a variant of the conventional connected components algorithm which we call delta-Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations.) Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.
Parallel Algorithms, Image Processing, Region Growing, Image Enhancement, Image Segmentation, Symmetric Neighborhood Filter, Connected Components, Parallel Performance
D. A. Bader, a. L. Davis, D. Harwood and J. JaJa, "Parallel Algorithms for Image Enhancement and Segmentation by Region Growing with an Experimental Study," Parallel Processing Symposium, International(IPPS), Honolulu, HI, 1996, pp. 414.