Regular Articles

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

[+] Author Affiliations
Johel Mitéran

Université de Bourgogne, Le2i Aile des Sciences de l’ingénieur, BP 47870 21078 Dijon, France

Sebastien Bouillant

Université de Bourgogne, Le2i Aile des Sciences de l’ingénieur, BP 47870 21078 Dijon, France

Michel Paindavoine

Université de Bourgogne, Le2i Aile des Sciences de l’ingénieur, BP 47870 21078 Dijon, France

Fabrice Mériaudeau

Université de Bourgogne, Le2i Aile des Sciences de l’ingénieur, BP 47870 21078 Dijon, France

Julien Dubois

Université de Bourgogne, Le2i Aile des Sciences de l’ingénieur, BP 47870 21078 Dijon, France

J. Electron. Imaging. 15(1), 013018 (March 13, 2006). doi:10.1117/1.2179436
History: Received March 04, 2005; Revised August 31, 2005; Accepted September 06, 2005; Published March 13, 2006
Text Size: A A A

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.

Figures in this Article
© 2006 SPIE and IS&T

Citation

Johel Mitéran ; Sebastien Bouillant ; Michel Paindavoine ; Fabrice Mériaudeau and Julien Dubois
"Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method", J. Electron. Imaging. 15(1), 013018 (March 13, 2006). ; http://dx.doi.org/10.1117/1.2179436


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.