Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7106
Title: Rotation invariant roughness features for texture classification
Authors: Kasparis, Takis 
Charalampidis, Dimitrios
Keywords: Fractals
Mathematical models
Image analysis
Classification
Issue Date: 2002
Publisher: IEEE
Source: IEEE International Conference on Acoustic, Speech, and Signal Processing, 2002, Orlando, Florida
Abstract: In this paper, we introduce a rotational invariant feature set for texture classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single scale features are combined with multiple scale features for a more complete textural representation. Directional wavelets are employed for the computation of roughness features, because of their ability to extract information at different resolutions and directions. The final feature vector is rotational invariant and retains the texture directional information. The roughness feature set results in higher classification rate than other feature vectors presented in this work, while preserving the important properties of FD, namely insensitivity to absolute illumination and contrast.
URI: http://ktisis.cut.ac.cy/handle/10488/7106
ISSN: 15206149
DOI: 10.1109/ICASSP.2002.5745452
Rights: © 2002 IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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