3D surface inspection using coupled HMMs

Author

Pernkopf, Franz

Author_Institution

Inst. of Signal Process. & Speech Commun., Graz Univ. of Technol., Austria

Volume

3

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

223

Abstract

This paper proposes coupled hidden Markov models (CHMM) for analysis of steel surfaces containing three-dimensional flaws. Due to scale on the surface, the reflection property across the intact surface changes and intensity imaging fails. Hence, the light sectioning method is used to acquire the surface range data. The steel block is vibrating on the conveyor during data acquisition which complicates the task. After depth map recovery and feature extraction, segments of the surface are classified by means of CHMMs. We present classification results of the CHMM and compare them to the naive Bayes classifier. The CHMM outperforms the naive Bayes approach.

Keywords

automatic optical inspection; feature extraction; flaw detection; hidden Markov models; pattern classification; steel; steel industry; 3D surface inspection; coupled HMM; coupled hidden Markov models; depth map recovery; feature extraction; light sectioning method; reflection property; steel block; steel surface analysis; steel surface classification; three dimensional flaw; Data acquisition; Feature extraction; Hidden Markov models; Image segmentation; Inspection; Optical reflection; Shape; Steel; Surface treatment; Vibrations;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334508

Filename

1334508