In this paper, a new defect detection algorithm for textured images is presented. The algorithm is based on the subband decomposition of gray level images through wavelet filters and extraction of the co-occurrence features from the subband images. Detection of defects within the inspected texture is performed by partitioning the textured image into non-overlapping subwindows and classifying each subwindow as defective or nondefective with a mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of this algorithm for the visual inspection of textile products obtained from the real factory environment are also presented.
Texture defect detection using subband domain co-occurrence matrices
01.01.1998
777552 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Texture Defect Detection Using Subband Domain Co-Occurrence Matrices
British Library Conference Proceedings | 1998
|An efficient method for texture defect detection: sub-band domain co-occurrence matrices
British Library Online Contents | 2000
|Defect detection in textile fabric images using subband domain subspace analysis
British Library Online Contents | 2007
|Color texture analysis using CFA chromatic co-occurrence matrices
British Library Online Contents | 2013
|Block-based Ordinal Co-occurrence Matrices for Texture Similarity Evaluation
British Library Conference Proceedings | 2005
|