A parallel algorithm for determining two-dimensional object positions using incomplete information about their boundaries

-Extraction of two-dimensional object locations using current techniques is a computationally intensive process. In this paper a parallel algorithm is presented that can specify the location of objects from edge streaks produced by an edge operator. Best-first searches are carried out in a number of non-interacting and localized edge streak spaces. The outcome of each search is a hypothesis. Each edge streak votes for a single hypothesis; it may also take part in the formation of other hypotheses. A poll of the votes determined the stronger hypotheses. The algorithm can be used as a front end to a visual pattern recognition system where features are extracted from the hypothesized object boundary or from the area localized by the hypothesized boundary. Experimental results from a biomedical domain are presented. Computer vision Two-dimensional object recognition Search Automated histopathology Edge-linking Model-based recognition Parallel algorithm Feature-learning