An adaptive fabric defect segmentation method based on a simplified PCNN
Fabric defect segmentation from an image has been proved to be a difficult task due to the influence of environment (e. g. illumination and noise, etc.) and various kinds of weave textures. Based on the property of variation with weft or warp direction in woven fabric defect, fabric images are described with the features whose variability degrees of gray contrast gradient of fabric image for segmentation relative to that of no-defective fabric image are first extracted before segmentation process. Then a simplified Pulse Coupled Neural Network (PCNN) with the parameters determined by spatial distributing information and feature data from an image is applied to adaptively segment the images. Experimental results show that the proposed method can segment common fabric defects quickly and correctly.
fabric defect variability degree PCNN synchronization
Shi Meihong Zhang Junying Sun Xia Gao Xiao-juan
School of Computer Science, Xian Polytechnic University, Xian 710048, China School of Computer Science and Egnineering, Xidian University, Xian 710071, China School of Computer Science, Xian Jiaotong University, Xian 710048, China
国际会议
2006中国国际毛纺织会议暨IWTO羊毛论坛(2006 China International Wool Textile Conference & IWTO Wool Forum)
西安
英文
266-274
2006-11-19（万方平台首次上网日期，不代表论文的发表时间）