NDLI: Impact of object extraction methods on classification performance in surface inspection systems

Content Provider	Springer Nature Link
Author	Raiser, Stefan Lughofer, Edwin Eitzinger, Christian Smith, James Edward
Copyright Year	2009
Abstract	In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single ‘suspicious’ region or the investigated part as a whole. After an image is acquired by the machine vision hardware, all pixels that deviate from a pre-defined ‘ideal’ master image are set to a non-zero value, depending on the magnitude of deviation. This procedure leads to so-called “contrast images”, in which accumulations of bright pixels may appear, representing potentially defective areas. In this paper, various methods are presented for grouping these bright pixels together into meaningful objects, ranging from classical image processing techniques to machine-learning-based clustering approaches. One important issue here is to find reasonable groupings even for non-connected and widespread objects. In general, these objects correspond either to real faults or to pseudo-errors that do not affect the surface quality at all. The impact of different extraction methods on the accuracy of image classifiers will be studied. The classifiers are trained with feature vectors calculated for the extracted objects found in images labeled by the user and showing surfaces of production items. In our investigation artificially created contrast images will be considered as well as real ones recorded on-line at a CD imprint production and at an egg inspection system.
Starting Page	627
Ending Page	641
Page Count	15
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	21
Issue Number	5
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-08-27
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Surface inspection Contrast images Object extraction Clustering Image classifiers Communications Engineering, Networks Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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