The paper describes a method of classifying natural textures based on maximum likelihood parameter estimation technique. The wavelet transform (WT) is used to represent the textural images in multiresolution. Co-occurrence matrices are then computed for the different scales of the wavelet transform and textural features are obtained from the co-occurrence matrices. Then a maximum likelihood classifier is designed using a set of training texture samples. Ten different Brodot textures have been classified using this procedure with an average classification accuracy of 99.7.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A maximum likelihood approach to texture classification using wavelet transform


    Contributors:


    Publication date :

    1994-01-01


    Size :

    420444 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Maximum Likelihood Approach to Texture Classification using Wavelet Transform

    Thyagarajan, K. S. / Nguyen, T. / Persons, C. E. et al. | British Library Conference Proceedings | 1994


    Texture classification using partial differential equation approach and wavelet transform

    Hiremath, P. S. / Bhusnurmath, R. A. | British Library Online Contents | 2017


    Texture Classification using Dual-Tree Complex Wavelet Transform

    Hatipoglu, S. / Mitra, S. K. / Kingsbury, N. et al. | British Library Conference Proceedings | 1999



    Texture Classification Using Neural Networks and Discrete Wavelet Transform

    Schumacher, P. / Zhang, J. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994