Modeling textures using Gaussian Markov random fields (GMRF) has been successfully used in classifying textures. However, these models do not perform well for self-similar textures such as those generated from fractional Brownian motion. The authors show that by using the difference images at different scales instead of the original image, one can significantly increase the performance of classifying self-similar texture patterns using GMRF models.<>


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    Title :

    Multi-resolution texture analysis of self-similar textures using hierarchical Gaussian Markov random field models


    Contributors:


    Publication date :

    1994-01-01


    Size :

    303460 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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