Research on defect recognition of textile laser printing image based on sparse optimization
By identifying defects in textile laser printing images,the ability to detect the quality of textile printing is improved.A sparse optimization based method for identifying defects in textile laser printing images is proposed.Different pixel block size feature matching methods are used to achieve defect detection and saliency parameter analysis in textile laser printing.Using texture feature matching method to extract and match feature points of textile printing lace,a sparse feature matching feature detection model for textile laser printing is established based on the difference in texture distribution between cotton threads.Based on the visual feature expression ability of printing lace itself and production,combined with position,scale The feature matching result of rotation invariant realizes defect recognition and detection of textile laser printing image.The test results show that the feature matching ability of using this method for defect recognition in textile laser printing images is good,and the dynamic detection ability of defect parts is strong.It has good ability to screen out false feature points in images and detect features.
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