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Baum-Eagon inequality in probabilistic labeling problems

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Author Info
Crescenzio Gallo (Università di Foggia-Dipartimento di Scienze Economiche, Matematiche e Statistiche)
Giancarlo de Stasio (Università di Foggia)

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Abstract

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.

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File URL: http://129.3.20.41/eps/exp/papers/0509/0509003.pdf
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Publisher Info
Paper provided by EconWPA in its series Experimental with number 0509003.

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Length: 12 pages
Date of creation: 07 Sep 2005
Date of revision:
Handle: RePEc:wpa:wuwpex:0509003

Note: Type of Document - pdf; pages: 12
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Web page: http://129.3.20.41

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Related research
Keywords: labeling; artificial intelligence; edge detection; probabilistic algorithms; pixel classification;

Find related papers by JEL classification:
C9 - Mathematical and Quantitative Methods - - Design of Experiments

This paper has been announced in the following NEP Reports:

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This page was last updated on 2009-11-25.

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