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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    Titel :

    Texture defect detection using subband domain co-occurrence matrices


    Beteiligte:
    Amet, A.L. (Autor:in) / Ertuzun, A. (Autor:in) / Ercil, A. (Autor:in)


    Erscheinungsdatum :

    01.01.1998


    Format / Umfang :

    777552 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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