Abstract

This paper presents a novel approach of visual inspection for texture surface defects. The approach uses artificial immune theory in learning the detection of texture defects. In this paper, texture defects are regards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholds used for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from the natural immune system is employed to learn antibodies. Experimental results on textile image inspection are presented to illustrate the merit and feasibility of the proposed method.

© 2005 Chinese Optics Letters

PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription