While some image textures can be changed with scale, others cannot. We focus on a multi-scale features of determing the sensitivity of the texture intensity to change. This paper presents a new method of texture structure classification and depth estimation using multi-scale features extracted from a higher order of the local autocorrelation functions. Multi-scale features consist of the meansand variances of distributions, which are extracted from theautocorrelation feature vectors according to multi-level scale. In order to reduce dimensional feature vectors, we employ the Principal Component Analysis (PCA) in the autocorrelation feature space. Each training image texture makes its own multi-scale model in a reduced PCA feature space, and the test of the texture image is projected in the homogeneous PCA space of the training data. The experimental results show that the proposed multi-scale feature can be utilized notonly for texture classification, but also depth estimation in two dimensional images with texture gradients.


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    Titel :

    Texture Structure Classification and Depth Estimation using Multi-Scale Local Autocorrelation Features


    Beteiligte:
    Kang, Yousun (Autor:in) / Hasegawa, Osamu (Autor:in) / Nagahashi, Hiroshi (Autor:in)


    Erscheinungsdatum :

    2003-06-01


    Format / Umfang :

    973305 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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