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Proceedings Paper

Progressive Knowledge Use In Incremental Segmentation
Author(s): Stephen Shemlon; Stanley M. Dunn; Tajen Liang
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Paper Abstract

The segmentation problem in image processing still falls short of an optimal solution. Obtaining a suitable segmentation requires domain specific knowledge. The optimal insertion of this knowledge in the segmentation process is the major issue. The complex human visual system first extracts reliable intrinsic information from the input and then applies knowledge stepwise at each stage of visual processing from the retinal to the cognitive levels. A near optimal segmentation scheme should approximate this approach. In this paper we present a segmentation algorithm based on the above approach. Using only intrinsic information from a noise refined copy of the input image, we identify and group pixels that are geometrically related into regions. The partitioning is successively refined by region merging, using only general rules of perceptual grouping. Control strategies for judging connectivity and homogeneity are based on basic topological and geometric rules. This general geometry guided segmentation has the advantage of being domain independent. At an intermediate level we start introducing domain specific knowledge in further region merging. Our applications are in cell physiology and we first exploit general knowledge from cytology. Progressively, we increase knowledge use in the definition of merging rules. Higher up the segmentation heirarchy, we include rules specific to a given branch of cell physiology and directly linked to characteristics of cell organelle. Merging stops when all existing regions are matchable with particular features and/or further merging is senseless or impossible using the defined rules. The results presented are from insect physiology where for some time now there has been disagreement among researchers on the number, type and distinguishing characteristics of the various hemocytes identified so far. The idea is to bring the various parties to a compromise by producing a Computer Based Reference Classification of Hemocytes.

Paper Details

Date Published: 21 March 1989
PDF: 12 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969275
Show Author Affiliations
Stephen Shemlon, Rutgers University (United States)
Stanley M. Dunn, Rutgers University (United States)
Tajen Liang, Rutgers University (United States)


Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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