[직/편성물] Decision Fusion for Visual Inspection of Textiles 출판일 : 2010.11.01 저자 : A.S. Tolba, H.A. Khan, A.M. Mutawa, and S.M. Alsaleem 서지사항 : Textile Reseach Journal, Volume 80, Issue 19, 2094 페이지 등록일 : 2011.03.28 I 조회수 : 624 작성자 : admin |
Defect detection of textiles is a necessary requirement for quality control and customer
satisfaction. This paper presents a system for decision fusion in order to enhance the
accuracy of defect detection in textiles. A multi-classifier decision fusion technique based
on majority voting is presented to solve the problems of sensitivity to parameter variation and
to make use of the advantages of the individual feature sets for accurate texture
characterization. Features based on Gray Level Co-occurrence Matrix (GLCM), Laws
Energy (LE) Filter, Local Binary Patterns (LBP), HU Moment invariants, Moment of Inertia
(MOI) and Standard Deviation of Gray levels are used to train a set of Learning Vector
Quantization (LVQ) classifiers. Detection accuracies of classifiers trained on single-feature
sets are compared with those of the majority voting among the individual classifiers. The
results obtained from majority voting indicate that the decision fusion technique improves the
accuracy and reliability of the detection process. Empirical results indicate the high
accuracy of the presented approach. The correct defect detection rate of the proposed
approach is 98.64% with an average false acceptance rate of 0.0012.
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