Baum-Eagon inequality in probabilistic labeling problems

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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Year of publication:	2005-09-07
Authors:	Gallo, Crescenzio ; Stasio, Giancarlo de
Institutions:	EconWPA
Subject:	labeling \| artificial intelligence \| edge detection \| probabilistic algorithms \| pixel classification

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Extent:	application/pdf
Series:	Experimental.
Type of publication:	Book / Working Paper
Notes:	Type of Document - pdf; pages: 12 12 pages
Classification:	C9 - Design of Experiments
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005125566