Baum-Eagon inequality in probabilistic labeling problems
AbstractThis work illustrates an approach to the study of labeling, aka 'object classification'. This kind of parallel computing problem well suites to AI applications (pattern recognition, edge detection, etc.) Our target consists in simplifying an overly computationally costly algorithm proposed by Faugeras and Berthod; using Baum-Eagon theorem, we obtained a reduced algorithm which produces results comparable with other more complex approaches.
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Bibliographic InfoPaper provided by EconWPA in its series Experimental with number 0509003.
Length: 12 pages
Date of creation: 07 Sep 2005
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Note: Type of Document - pdf; pages: 12
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labeling; artificial intelligence; edge detection; probabilistic algorithms; pixel classification;
Find related papers by JEL classification:
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