基于极线几何的统计优化特征匹配算法

doi:10.7527/S1000-6893.2018.21727

Abstract

Abstract: To solve the problems of small matching number and poor matching with repetitive structures in image matching based on feature points, a statistical optimization feature matching algorithm is proposed based on the epipolar geometry. As correct matching feature points satisfy the epipolar constraint, the feature points' search region can be reduced and the mismatches caused by repetitive structures can be avoided. Our approach first uses a small sample of features to estimate the fundamental matrix between images and leverages it for guiding feature matching based the epipolar constraint model. Then we use the statistical optimization method based on scale and main direction information of feature points to further eliminate mismatches and get the final matching results. Experimental results show that our algorithm also has good robustness against image rotation and scale transformation, and the matching precision and number have been greatly improved. Our algorithm is also effective for image matching with repetitive structures.

Key words: feature matching, epipolar geometry, main direction, scale, Sampson distance

CLC Number:

V419
TP391.4

ZHAO Chunhui, FAN Bin, TIAN Limin, HU Jinwen, PAN Quan. Statistical optimization feature matching algorithm based on epipolar geometry[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(5): 321727-321727.

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Statistical optimization feature matching algorithm based on epipolar geometry

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