Title
Inspection of specular surfaces using optimized M-channel wavelets
Abstract
Despite its age the inspection of specular surfaces is still a topic of ongoing research. While sensory approaches to inspect such surfaces based on deflectometry are increasingly used in practice, the evaluation techniques using the acquired signals (images and reconstruction results) are often not sufficient. This work addresses the challenge of detecting defects with different characteristics on specular surfaces by using robust multiscale detection and classification. In order to process the signals obtained by deflectometry efficiently in all relevant scales, a method for generating an optimized biorthogonal wavelet filter bank with strong correlation to any number of anomaly classes is proposed. The filter bank is optimized for each defect class to obtain a sparse scale space representation. In addition a Bayesian classification approach is presented to classify defects like dents and pimples directly in the scale space.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6638086
ICASSP
Keywords
Field
DocType
optimized biorthogonal wavelet filter bank,automatic optical inspection,bayesian classification,surface topography,wavelet transforms,bayes methods,sparse scale space representation,strong correlation,specular surface inspection,deflectometry,optimized filters,channel bank filters,sensory approach,signal classification,robust multiscale classification,signal reconstruction,optimized m-channel wavelets,robust multiscale detection,signal detection,correlation methods,wavelet-transform,feature extraction,wavelet transform,surface waves
Computer vision,Pattern recognition,Detection theory,Computer science,Filter bank,Specular reflection,Scale space,Artificial intelligence,Signal reconstruction,Wavelet transform,Wavelet,Biorthogonal wavelet
Conference
ISSN
Citations 
PageRank 
1520-6149
1
0.36
References 
Authors
6
4
Name
Order
Citations
PageRank
Tan-Toan Le111.04
Mathias Ziebarth221.81
Thomas Greiner3347.48
Michael Heizmann4219.11