Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-Invariant texture classification using steerable gabor filter bank
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Computer Vision and Image Understanding
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In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space.