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Blind detection of fabric defects using multiple image features
Author(s): Karam Park; Sangyup Oh; Sang Hwa Lee; Nam Ik Cho
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Paper Abstract

This paper proposes a blind detection of fabric defects using multiple image features. The aim of proposed method is to detect the new types of fabric defects that have not been learned. For general learning of image features, this paper first learns the characteristics of image features between normal and defective image patches for various types of fabric structures and defects. The image features are frequency coefficients, color histogram, and edge orientation histogram. The mean vectors of 3 image features are calculated, and the vector distance distributions of normal and defective patches are learned using support vector machine. Since the decision boundary is determined from general distributions of image features, the proposed method is not restricted to the detect types and fabric structures which have not used in the learning phase. According to experiments with the real fabric images and defects types that have not been learned, the proposed method detects fabric defects with 96.4% accuracy at 0.4% failure including false positive and false negative errors. This result outperforms the usual CNN (Convolutional Neural Network) approaches.

Paper Details

Date Published: 22 March 2019
PDF: 5 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493S (22 March 2019); doi: 10.1117/12.2521548
Show Author Affiliations
Karam Park, Seoul National Univ. (Korea, Republic of)
Sangyup Oh, Seoul National Univ. (Korea, Republic of)
Sang Hwa Lee, Seoul National Univ. (Korea, Republic of)
Nam Ik Cho, Seoul National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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