An approach to defect detection in materials characterized by complex textures
Pattern Recognition
A taxonomy for texture description and identification
A taxonomy for texture description and identification
Robot Vision
Machine Learning
Motif-based defect detection for patterned fabric
Pattern Recognition
Ellipsoidal decision regions for motif-based patterned fabric defect detection
Pattern Recognition
Review article: Automated fabric defect detection-A review
Image and Vision Computing
A new approach for wet blue leather defect segmentation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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This paper describes an automated vision system for detecting and classifying surface defects on leather fabric. In the defect inspection process, visual defects are located and reported through a two-step segmentation procedure based on thresholding and morphological processing. In the defect classification process, the system utilizes both geometric and statistical features as its feature sets; that is, a new normalized compactness measure, and first- and second-order statistical features. In an effort to maximize the classification efficiency, a three-stage sequential decision-tree classifier is adopted for the classification of five types of defects: lines, holes, stains, wears, and knots. If line defects are identified as a result of classification, they are checked by a line combination algorithm to determine if they are parts of larger line defects and, in such a case, are reported as combined line defects. Satisfactory results were achieved in the classification test with an overall accuracy of 91.25%