In D. P. Casasent (Ed.), Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, Proc. SPIE 3522, pp. 96-103, 1998.

Abstract

A surface inspection problem is divided into three parts, into an image acquisition part, into a defect detection part that is suitable for hardware implementation, and into a defect classification part that is done in a user's terminal. In the defect detection part extraction of texture features is done and potential defect areas are marked. The proposed scheme is taught only with examples of fault-free surface. In the defect classification part features describing the shape and internal structure of defects are extracted and defects are classified into different defect classes. Examples of defects are used to train the classification system. Use of the self-organizing map (SOM) in defect detection and in defect classification makes the proposed method adaptable to different types of surfaces and to different types of defects. Only reselection of features may be necessary to cope with different surface and defect characteristics. The results of experiments with base paper samples are encouraging.