Baum-Eagon inequality in probabilistic labeling problems
Crescenzio Gallo () and
Giancarlo de Stasio
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Giancarlo de Stasio: Università di Foggia
Experimental from University Library of Munich, Germany
This 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.
Keywords: labeling; artificial intelligence; edge detection; probabilistic algorithms; pixel classification (search for similar items in EconPapers)
JEL-codes: C9 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
Note: Type of Document - pdf; pages: 12
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpex:0509003
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