Independent Component Analysis for Texture Defect Detection O. Gökhan Sezer 1 , Ayın Ertüzün 1 , Aytül Erçil 2 1 Boğaziçi University, Electrical and Electronics Engineering Department, Istanbul-Turkey 2 Sabancı University, Faculty of Engineering and Natural Sciences, Istanbul-Turkey ogsezer@hotmail.com, ertuz@boun.edu.tr, aytulercil@sabanciuniv.edu ABSTRACT In this paper, a novel method for texture defect detection is presented. The method makes use of Independent Component Analysis (ICA) for feature extraction from the non- overlapping subwindows of texture images and classifies a subwindow as defective or non- defective according to Euclidean distance between the feature obtained from average value of the features of a defect free sample and the feature obtained from one subwindow of a test image. The experimental results demonstrating the use of this method for visual inspection of textile products obtained from a real factory environment are also presented. I- INTRODUCTION Defect detection from images plays significant role in quality of manufactured products and its application areas continue to increase. Numerous methods have been proposed for performing this task. Amet et.al. [1] have used sub-band domain co-occurrence matrices for texture defect detection, Karras et.al. [11] have suggested focusing on detecting defects from images' wavelet transformation and vector quantization related properties of the associated wavelet coefficients, Chetverikov et.al. [4] have approached the texture defect detection problem in a more theoretical way, based on regularity and orientation criteria. Chen and Jain used a structural approach to defect detection in textured images. Dewaele et.al. used signal processing methods to detect point defects and line defects in texture images. Cohen et.al. [5] used MRF models for defect inspection of textile surfaces while Erçil et.al. [6] used similar techniques for inspection of painted metallic surfaces. Atalay has implemented MRF model-