Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33836
Title: MONOGENIC-LBP : a new approach for rotation invariant texture classification
Authors: Zhang, L 
Zhang, L 
Guo, Z
Zhang, D 
Issue Date: 2010
Source: Proceedings - International Conference on Image Processing, ICIP, 2010, p. 2677-2680
Abstract: Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2 nd-order Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.
Keywords: LBP
Monogenic signal
Texture classification
ISBN: 9781424479948
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5651885
Description: 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010
Appears in Collections:Conference Paper

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