DocumentCode :
467771
Title :
Fabric Defects Segmentation Approach Based on Texture Primitive
Author :
Zhu, Shuang-Wu ; Hao, Hong-Yang ; Li, Peng-yang ; Shi, Mei-Hong ; Qi, Hua
Author_Institution :
Northwestern Polytech. Univ., Xi´´an
Volume :
3
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1596
Lastpage :
1600
Abstract :
A new fabric defects segmentation approach based on texture primitive is put forward in the paper, which consists of four steps: 1) calculating texture primitive template using auto-correlation function; 2) enhancing defect image through calculating difference between each texture primitive and primitive template; 3) constructing mean gradation image to attenuate the high frequent background information; 4) segmenting defect images according to threshold values acquired automatically through Otsu´s approach, etc. Validity and robustness of the approach were proved by different fabric defect images segmentation experiment.
Keywords :
correlation methods; fabrics; image segmentation; image texture; production engineering computing; autocorrelation function; fabric defects segmentation; high frequent background information; mean gradation image; texture primitive; Autocorrelation; Cybernetics; Educational institutions; Fabrics; Image processing; Image segmentation; Machine learning; Robustness; Testing; Textiles; Defect segmentation; Fabric detection; Image enhancement; Texture primitive;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370400
Filename :
4370400
Link To Document :
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