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Road sing classification using Laplace kernel classifier
- 1.0410431 - UTIA-B 20000147 RIV NL eng J - Journal Article
Paclík, Pavel - Novovičová, Jana - Pudil, Pavel - Somol, Petr
Road sing classification using Laplace kernel classifier.
Pattern Recognition Letters. Roč. 21, 13/14 (2000), s. 1165-1173. ISSN 0167-8655. E-ISSN 1872-7344
Grant - others:MŠMT(CZ) VS96063; GA AV(CZ) IAA2075608; GA AV(CZ) IAA2075606
Program: IA; IA
Institutional research plan: AV0Z1075907
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.346, year: 2000
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.
Permanent Link: http://hdl.handle.net/11104/0130520
Number of the records: 1