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Proceedings Paper

A robust texture descriptor based on a gradient orientation and modulus matrix
Author(s): Wenwu Wang; Zhiguo Cao
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Paper Abstract

We propose a novel robust texture descriptor, the Gradient Orientation and Modulus Matrix (GOMM), which is based on the fact that human perception of an image pattern depends not only on its intensity, but also on changes in intensity and regularity (such as the gradient modulus and gradient orientation of the image).
A GOMM is constructed in three steps. First, the gradient orientation of each pixel is mapped onto N intervals and the gradient modulus is partitioned into M levels. Next, a block is constructed from the gradient modulus of pixels whose gradient orientations are mapped onto the same interval. Then, each component of the GOMM is given by the sum of the ratios between two terms, namely, the differential gradient modulus grading between the elements in the above-mentioned block, and the distance between the elements. Finally, a six-dimension vector is calculated from each GOMM. By rearranging feature vectors from each GOMM, we can concatenate the vectors to construct a uniform GOMM feature for a given image, irrespective of the angle of the image. Experimental results on the KTH-TIPS2 (The Royal Institute of Technology - Textures under varying Illumination, Pose and Scale) image database show that the GOMM significantly outperforms the other classical descriptors.

Paper Details

Date Published: 26 October 2013
PDF: 8 pages
Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 891805 (26 October 2013); doi: 10.1117/12.2030174
Show Author Affiliations
Wenwu Wang, Huazhong Univ. of Science and Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8918:
MIPPR 2013: Automatic Target Recognition and Navigation
Tianxu Zhang; Nong Sang, Editor(s)

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