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.


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    Title :

    Texture defect detection using subband domain co-occurrence matrices


    Contributors:
    Amet, A.L. (author) / Ertuzun, A. (author) / Ercil, A. (author)


    Publication date :

    1998-01-01


    Size :

    777552 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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