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.

Baum-Eagon inequality in probabilistic labeling problems

GALLO, CRESCENZIO;
2006

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11369/10545
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