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 2nd-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.
Original language | English |
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Title of host publication | 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings |
Pages | 2677-2680 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2010 |
Event | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010 |
Conference
Conference | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 26/09/10 → 29/09/10 |
Keywords
- LBP
- Monogenic signal
- Texture classification
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing