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Machine vision algorithm for fabric inspection

V. Asha

Abstract


Inspection is an important stage in quality control in fabric industries. In this paper, a machine vision based algorithm is presented for inspecting fabrics containing patterned textures. The input textures with different patterns are divided into several portions of size same as periodic pattern size whose dimensions are determined based on superposition of Distance Matching Function. Defective portions are identified based on dendrogram resulting from cluster analysis in multi-dimensional feature space using first order statistics and co-occurrence matrix properties. An ideal texture serving as reference/template image is constructed by retaining the defect-free portions and replacing the defective portions by arithmetic mean of all defect-free portions. Traditional image subtraction between the input texture and the template image followed by average-filtering and thresholding help in identifying exact locations of the defects. Experimentations with real fabric images reveal the capability of the proposed model for fabric inspection.

Keywords


Fabrics, Patterns, Defects, Clusters, Statistics.

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