DocumentCode
2502036
Title
Generalized formulation and hypercube algorithms for relaxation labeling
Author
Leung, Eva ; Li, Xiaobo
Author_Institution
Dept. of Comput. Sci., Keyano Coll., Fort McMurray, Alta., Canada
fYear
1991
fDate
30 Apr-2 May 1991
Firstpage
64
Lastpage
69
Abstract
Presents a generalized formulation for several well-known approaches to relaxation labeling, including discrete, fuzzy, linear probabilistic models and several nonlinear probabilistic modes. Based on this generalized framework, two parallel algorithms for SIMD hypercube computers with different numbers of processors are proposed and analyzed. The algorithms achieve minimal time complexity
Keywords
computational complexity; parallel algorithms; relaxation theory; SIMD hypercube computers; discrete probabilistic models; fuzzy probabilistic models; hypercube algorithms; linear probabilistic models; minimal time complexity; nonlinear probabilistic modes; parallel algorithms; relaxation labeling; Algorithm design and analysis; Computer science; Computer vision; Concurrent computing; Educational institutions; Filtering; Fuzzy sets; Hypercubes; Labeling; Parallel algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel Processing Symposium, 1991. Proceedings., Fifth International
Conference_Location
Anaheim, CA
Print_ISBN
0-8186-9167-0
Type
conf
DOI
10.1109/IPPS.1991.153758
Filename
153758
Link To Document