Siegmund, DirkDirkSiegmundKähm, OlgaOlgaKähmHandtke, DavidDavidHandtke2022-03-132022-03-132016https://publica.fraunhofer.de/handle/publica/39317510.5220/00059693009901052-s2.0-85005951023Research on the quality assurance of textiles has been a subject of much interest, particularly in relation to defect detection and the classification of woven fibers. Known systems require the fabric to be flat and spread-out on 2D surfaces in order for it to be classified. Unlike other systems, this system is able to classify textiles when they are presented in piles and in assembly-line like environments. Technical approaches have been selected under the aspects of speed and accuracy using 2D camera image data. A patch-based solution was chosen using an entropy-based pre-selection of small image patches. Interest points as well as texture descriptors combined with principle component analysis were part of this evaluation. The results showed that a classification of image patches resulted in less computational cost but reduced accuracy by 3.67%.enquality assurancepattern recognitionclassification methodsclassification performancefabricsdefect detectionLead Topic: Digitized WorkResearch Line: Computer vision (CV)006conference paper