一种新的宽基线图像匹配方法

doi:10.3969/j.issn.1001-2400.2011.02.021

Abstract

Abstract:

In order to overcome the differences in 3D viewpoint, scale, rotation and grayscale, and achieve stable wide baseline image matching, a new feature descriptor based on Local Binary Pattern Histogram Fourier features is constructed and a new wide baseline image matching approach is proposed according to comparison and analysis. First, Maximally Stable Extremal Regions (MSER) of the reference image and real-time image which are scale and affine invariant are extracted, respectively. Second, rotation and grayscale invariant descriptors are constructed. Then the matching MSER features of the two images are extracted based on the nearest neighbor Euclid distance ratio strategy. The epipolar geometry of the two images is estimated according to the Progressive Sample Consensus (PROSAC) algorithm. Simulation results show that the proposed method is robust to changes in 3D viewpoints, scale, rotation and grayscale, and can achieve stable wide baseline image matching.

Key words: machine vision, wide baseline image matching, maximally stable extremal regions, local binary pattern histogram Fourier features, feature matching, epipolar geometry

CHEN Bing;ZHAO Yigong;LI Xin. Wide baseline image matching based on a new local descriptor[J].J4, 2011, 38(2): 116-123.

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Wide baseline image matching based on a new local descriptor

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