EconPapers    
Economics at your fingertips  
 

Baum-Eagon inequality in probabilistic labeling problems

Crescenzio Gallo () and Giancarlo de Stasio
Additional contact information
Giancarlo de Stasio: Università di Foggia

Experimental from EconWPA

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.

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
Date: 2005-09-07
Note: Type of Document - pdf; pages: 12
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://econwpa.repec.org/eps/exp/papers/0509/0509003.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpex:0509003

Access Statistics for this paper

More papers in Experimental from EconWPA
Series data maintained by EconWPA ().

 
Page updated 2017-11-23
Handle: RePEc:wpa:wuwpex:0509003