Abstract To solve the problems of long operation time and low matching accuracy in the local invariant feature descriptor of speeded up robust features(SURF) algorithm, a image matching algorithm combining improved SURF algorithm with grid-based motion statistics(GMS) is proposed. Firstly, determinant of Hessian is utilized to determine the feature points of the image, and the main direction extraction method in SURF algorithm is improved by gradient direction to increase the accuracy of the main direction of the feature points. The binary feature descriptor rotation-aware binary robust independent elementary feature(rBRIEF) is employed to describe the feature points. Then, the feature points are roughly matched by Hamming distance. Finally, GMS is adopted to eliminate the mismatches. Experiment on Oxford VGG standard dataset indicates that the proposed algorithm achieves higher matching accuracy and efficiency with image changes in scale, illumination and rotation.
Fund:Supported by National Natural Science Foundation of China(No.51905405), Research Project of Engineering Science and Technology Talent Cultivation of Ministry of Education（No.18JDGC029）, Natural Science Foundation Research Program of Shanxi Province(No.2019JQ-855), Natural Science Project of Shanxi Provincial Department of Education(No.19JK0375)
Corresponding Authors:
WANG Xiaohua, Ph.D., professor. Her research interests include intelligent robot and pattern recognition.
About author:: FANG Qi, master student. His research interests include intelligent robot and pattern recognition.WANG Wenjie, Ph.D., associate profe-ssor. His research interests include robotics.
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2019, Vol. 32 
