棉纺织技术2017，Vol.45Issue(10)：1-4,4.
基于特征融合与低秩分解的织物疵点检测
Fabric Defect Detection Based on Feature Fusion and Low-rank Decomposition
摘要
Abstract
Fabric defect detection algorithm based on feature fusion and low-rank decomposition was researched.The original image was segmented to superpixel block by superpixel segmentation method.Gray feature and HOG feature of each pixel block was extracted respectively for building the fusion feature matrix.Fusion feature was segmented to low-rank background and salient defect by low-rank decomposition method.The defect saliency image was got based on the size of salience degree.Finally maximum entropy segmentation method was chosen to segment the saliency image and the detection result was got.TILDA standard fabric image database was chosen to test the validity of the algorithm.The results show that the suggested algorithm can detect the position and shape of fabric defect effectively.It is considered that the suggested algorithm has better self-adaptive ability.It is suitable for more defect types and has higher defect detection rate.关键词
织物疵点/特征融合/低秩分解/疵点检测/显著图分割Key words
Fabric Defect/Feature Fusion/Low-rank Decomposition/Defects Detection/Saliency Image Segmentation分类
轻工纺织引用本文复制引用
刘洲峰,闫磊,李春雷,董燕,王宝瑞..基于特征融合与低秩分解的织物疵点检测[J].棉纺织技术,2017,45(10):1-4,4.基金项目
国家自然科学基金资助项目(61379113) （61379113）
郑州市科技领军人才项目(131PLJRC643) （131PLJRC643）
河南省高校科技创新人才项目(17HASTIT019) （17HASTIT019）
