Paper Info
Title
Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method
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. (c) 2006 SPIE and IS&T.
Year
DOI
Venue
2006
10.1117/1.2179436
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
classification system,real time,algorithms,feature extraction,machine vision,inspection,decision tree,neural networks,support vector machine,feature selection
Hyperrectangle,Decision tree,Machine vision,Feature selection,Computer science,Artificial intelligence,Computer vision,Pattern recognition,Support vector machine,Feature extraction,Boosting (machine learning),Statistical classification,Machine learning
Journal
Volume
Issue
ISSN
15
1
1017-9909
Citations 
PageRank 
References 
0
0.34
11
Authors
5
Name
Order
Citations
PageRank
Johel Mitéran1475.52
Sebastien Bouillant200.68
Michel Paindavoine311521.70
Fabrice Meriaudeau442352.78
Julien Dubois514618.76