A parallel algorithm for determining two-dimensional object positions using incomplete information about their boundaries
dc.contributor.author | Amini, Amir A. | en_US |
dc.contributor.author | Weymouth, Terry E. | en_US |
dc.contributor.author | Anderson, David J. | en_US |
dc.date.accessioned | 2006-04-07T20:55:30Z | |
dc.date.available | 2006-04-07T20:55:30Z | |
dc.date.issued | 1989 | en_US |
dc.identifier.citation | Amini, Amir A., Weymouth, Terry E., Anderson, David J. (1989)."A parallel algorithm for determining two-dimensional object positions using incomplete information about their boundaries." Pattern Recognition 22(1): 21-28. <http://hdl.handle.net/2027.42/28103> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6V14-48MPNDG-1DN/2/109fafb492331c8a1a9762b9bdc4a438 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/28103 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 701161 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | A parallel algorithm for determining two-dimensional object positions using incomplete information about their boundaries | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2212, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2212, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2212, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/28103/1/0000551.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0031-3203(89)90034-4 | en_US |
dc.identifier.source | Pattern Recognition | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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