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Experimental results are presented by implementing algorithms from a motion estimation system using these techniques on a hypercube multiprocessor. Normally in a vision system a sequence of algorithms is employed in which output of an algorithm is input to the next algorithm in the sequence. The distribution of features computed as a by\u2010product of the current task is used to repartition the data for the next task in the system. This allows parallel computation of feature distribution, and therefore the overhead of estimating the load is kept small. It is observed that the communication overhead to repartition data using these run\u2010time decomposition techniques is very small. It is shown that significant performance improvements over uniform\u2010block\u2010oriented partitioning schemes are obtained.<\/jats:p>","DOI":"10.1002\/cpe.4330040404","type":"journal-article","created":{"date-parts":[[2006,11,18]],"date-time":"2006-11-18T06:31:12Z","timestamp":1163831472000},"page":"313-334","source":"Crossref","is-referenced-by-count":3,"title":["Run\u2010time data decomposition for parallel implementation of image processing and computer vision tasks"],"prefix":"10.1002","volume":"4","author":[{"given":"Alok N.","family":"Choudhary","sequence":"first","affiliation":[]},{"given":"Ravi","family":"Ponnusamy","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2006,10,24]]},"reference":[{"key":"e_1_2_1_2_2","unstructured":"C.Weems A.Hanson E.RisemanandA.Rosenfeld \u2018An integrated image understanding benchmark:recognition of a 2 1\/2 D mobile\u2019 International Conference on Computer Vision and Pattern Recognition Ann Arbor MI June1988."},{"key":"e_1_2_1_3_2","doi-asserted-by":"crossref","unstructured":"A. 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