UNSUPERVISED IMAGE SEGMENTATION WITH THE SELF-ORGANIZING MAP AND STATISTICAL
METHODS
Jukka Iivarinen and Ari Visa
In D. P. Casasent (Ed.), Intelligent Robots and Computer Vision XVII:
Algorithms, Techniques, and Active Vision, Proc. SPIE 3522, pp. 516-526, 1998.
Abstract
In this paper a special type of image segmentation, a two-class
segmentation, is considered. Defect detection in quality control
applications is a typical two-class problem. The main idea in this paper
is to train the two-class classifier with fault-free samples that is an
unexpected approach. The reason is that defects are rare and expensive.
The proposed defect detection is based on the following idea: an unknown
sample is classified as a defect if it differs enough from the estimated
prototypes of fault-free samples. The self-organizing map is used to
estimate these prototypes. Surface images are used to demonstrate the
proposed image segmentation procedure.
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