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Journal of Electronic Imaging

Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method
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Paper Abstract

We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boosting, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detecting anomalies under manufacturer production, as well as in classifying the anomalies among 20 listed categories. Manufacturer specifications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is addressed by using a classification system relying on real-time machine vision. To fulfill both real-time and quality constraints, three classification algorithms and a tree-based classification method are compared. The first one, hyperrectangle based, proves to be well adapted for real-time constraints. The second one is based on the Adaboost algorithm, and the third one, based on SVM, has a better power of generalization. Finally, a decision tree allowing improving classification performances is presented.

Paper Details

Date Published: 1 January 2006
PDF: 9 pages
J. Electron. Imag. 15(1) 013018 doi: 10.1117/1.2179436
Published in: Journal of Electronic Imaging Volume 15, Issue 1
Show Author Affiliations
Johel Miteran, Univ. de Bourgogne (France)
S. Bouillant, Univ. de Bourgogne (France)
Michel Paindavoine, Univ. de Bourgogne (France)
Fabrice Meriaudeau, Univ. de Bourgogne (France)
Julien Dubois, Univ. de Bourgogne (France)

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