Road sing classification using Laplace kernel classifier

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SYSNO ASEP	0410431
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Ostatní články
Title	Road sing classification using Laplace kernel classifier
Author(s)	Paclík, Pavel (UTIA-B) Novovičová, Jana (UTIA-B) Pudil, Pavel (UTIA-B)_RID Somol, Petr (UTIA-B)_RID
Source Title	Pattern Recognition Letters. - : Elsevier - ISSN 0167-8655 Roč. 21, 13/14 (2000), s. 1165-1173
Number of pages	9 s.
Language	eng - English
Country	NL - Netherlands
Subject RIV	BB - Applied Statistics, Operational Research
CEZ	1075907
Annotation	The Laplace kernel rule for the road sign classification based on a priori information about road signs grouping has been developed. The smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method using the Expectation-Maximization algorithm. The new classification algorithm has been successfully tested on more than 1100 images of 43 road sign types. The comparison with the Bayes classifier assuming the Gaussian mixtures has been made.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.

Number of the records: 1