A bidirectional image matching algorithm based on SIFT features

Nan Zhang; Lin Luo; Xiaorong Gao; Jinlong Li

doi:10.1117/12.2245696

25 October 2016 A bidirectional image matching algorithm based on SIFT features

Nan Zhang, Lin Luo, Xiaorong Gao, Jinlong Li

Author Affiliations +

Proceedings Volume 9686, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices; 968614 (2016) https://doi.org/10.1117/12.2245696
Event: Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), 2016, Suzhou, China

Abstract

Matching accuracy plays a vital role in image matching and image recognition. In order to improve the accuracy and robust of image matching, a bidirectional matching algorithm is proposed to delete false matching relationships, so the matching accuracy can be improved. Based on the unique constraint of unilateral matching, the positive matching results from template image to the image to be matched can be obtained. Then the negative matching results from the original image to be matched to the original template image can also be obtained. Now the final bidirectional matching results can be achieved by the intersection of the positive and negative results. Precision ratio is taken as the evaluation indicator. Through various image transformation scenes, experimental results show that the proposed algorithm has a higher precision ratio on the contrast of unilateral matching algorithm. So the proposed bidirectional matching algorithm can improve the precision ratio and robust of unilateral matching algorithm and improve the matching accuracy of image matching.

Citation Download Citation

Nan Zhang, Lin Luo, Xiaorong Gao, and Jinlong Li "A bidirectional image matching algorithm based on SIFT features", Proc. SPIE 9686, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices, 968614 (25 October 2016); https://doi.org/10.1117/12.2245696

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KEYWORDS

Feature extraction

Detection and tracking algorithms

Image processing

Reliability

Digital image processing

Medical imaging

Lithium

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