Image defect recognition based on "Super Fuzzy" characteristic

被引：1

作者：

Liu Z. ^{[1
]}

Wang X. ^{[1
]}

机构：

[1] Zhongyuan University of Technology, Zhengzhou

来源：

Journal of Multimedia | 2010年 / 5卷 / 02期

关键词：

Clustering; Defect region; Fuzzy; Recognition; Super fuzzy feature; Variable window;

D O I：

10.4304/jmm.5.2.181-188

中图分类号：

学科分类号：

摘要：

In this paper, we propose a new defects recognition algorithm for dynamic image based on "super fuzzy" feature. With this algorithm, the image is divided into some variable windows, and the eigenvector of each window is constructed. We introduce "super fuzzy" vector to make window vectors "super fuzzy" processing, thus the window feature has "super fuzzy" characteristic with the difference of the primary and secondary. Also we present window coefficient to adjust recognition speed and accuracy according to different images. Furthermore, objective function, membership function and clustering center calculation function of fuzzy clustering algorithm with window coefficient and "super fuzzy" vector are proposed in this paper. At last, we take example for fabric defects detection with this algorithm, list recognition results, discuss recognition result influence by "super fuzzy" feature and size change of window, and make some comparison with other algorithms. The conclusion shows that this algorithm can recognize more categories of image abnormal regions with high-accuracy, high-speed, no-training and extensive application. © 2010 ACADEMY PUBLISHER.

引用

页码：181 / 188

页数：7

共 35 条

[1]

Rohrmus D.R., Invariant and adaptive geometrical texture features for defect detection and classification, Pattern Recognition, 38, pp. 1546-1599, (2003)

[2]

Yun L., Jingmiao Z., Jianwei J., Fabric Defect Detection Method Based on Image Distance Difference, Microcomputer Information, 23, pp. 303-306, (2007)

[3]

Feng W., Guotai J., Ye D., Method of Fabric Defects Detection Based on Mathematical Morphology, Journal of Test and Measurement Technology, 21, pp. 515-518, (2007)

[4]

Tajeripour F., Kabir E., Sheikhi A., Fabric Defect Detection Using Modified Local Binary Patterns, Eurasip Journal On Advances In Signal Processing, 1, pp. 1-12, (2008)

[5]

Chao Z., Desen Z., Li X., Textural defect detection based on label co-occurrence matrixm, Journal of Huazhong University of Science and Technology(nature Science), 34, pp. 25-28, (2006)

[6]

Zhang Y.F., Bresee R.R., Fabric defect detection and classification using image analysis, Textile Research Journal, 65, pp. 1-9, (1995)

[7]

Chen P.-W., Classifying textile faults with a back- propagation neural network using power spectral, Textile Research Journal, 68, pp. 121-126, (1998)

[8]

Kwak C., Jose A.V., Tofang-Sazi K., A neural network approach for defect identification and classification on leather fabric, Journal of Intelligent Manufacturing, 11, pp. 485-499, (2000)

[9]

Jianli L., Baoqi Z., Identification of fabric defects based on discrete wavelet transform and back-propagation neural network, Journal of The Textile Institute, 98, pp. 355-362, (2007)

[10]

Zhu S.-W., Hao H.-Y., Li P.-Y., Et al., Fabric Defects Segmentation Approach Based on Texture Primitive, 2007 International Conference On Machine Learning and Cybernetics, 3, pp. 1596-1600, (2007)

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