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Recognition Algorithm Research of Fabric Defect On-line Detection
Author: ZhangJinLin
Tutor: LiYongï¼›ShangHuiChao
School: Zhongyuan Institute of Technology
Course: Mechanical and Electronic Engineering
Keywords: Fabric defects Feature Extraction Online testing Wavelet Transform Neural Networks
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 38
Quote: 0
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Abstract
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With the development of industrial automation , textile production automation is an inevitable trend . The ensuing product quality more and more attention , improve product quality, accelerate the study of automatic detection technology is a priority . The recognition algorithm based on machine vision technology fabric image online testing research , and the completion of the research and implementation of the gray fabric online testing key technologies . First , establish the evaluation criteria of the fabric classification and defect characteristics were analyzed , and provides a basis for recognition classification ; discusses fabric defect detection principle fabric defect detection software online system design , detailed analysis of the process of the image on fabric image pre-processing made ??a brief presentation . Secondly , after equalization , median filtering and dislocation poor shadow algorithm pretreatment , higher quality images , remove noise and reduce the impact of the fabric texture based structure straight square wave , wavelet transform and mathematical morphology learn defect feature extraction method . First image on the gray fabric in the space domain structure straight square wave and wavelet transform , the extracted image feature values ??, determines whether the defect . If the defects, and then the binary image threshold segmentation , mathematical morphology method to extract the geometric features of fabric defects for defect classification . Algorithm , first using the Lab VIEW software quickly and easily on algorithm analysis and programming using Visual C combined with optimized instruction set computer - based hardware IPP class library feature extraction algorithm . Finally, the identification and classification of fabric defects , proposed the establishment of two neural networks , a for discriminant fabric image if there is defect , the other is used to classify images containing defects . Standard BP network algorithm based on an improved BP algorithm , and applied to the gray fabric online testing , the results show that the improved BP algorithm to train speed , high accuracy of recognition classification .
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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