Information Fusion

Image stitching enhancement method with symmetrical constraint of local feature points

  • ZHONG Mengfan ,
  • PEI Jihong
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  • 1. College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518061, China

Received date: 2022-01-13

  Revised date: 2022-01-13

  Online published: 2022-02-18

Supported by

National Natural Science Foundation of China (62071303,61871269);Guangdong Basic and Applied Basic Research Foundation (2019A1515011861);Shenzhen Science and Technology Projection (JCYJ20190808151615540); China Postdoctoral Science Foundation (2021M702275)

Abstract

In this paper, an image mosaic enhancement method with symmetrical constraint of local feature points is proposed. Firstly, according to the internal and external parameter matrix of the camera, the forward local constraint region of the feature points is calculated, and the set of positive matching points is obtained. Then, the inverse local constraint region is calculated, and the set of inverse matching points is obtained. The intersection in the set of forward and reverse matching point pairs is found, and the final correct matching point pair set which satisfies the symmetric constraint condition is obtained. Combining this enhancement method with the existing advanced stitching model learning algorithm, the optimal image transformation is obtained, and finally the stitched image is obtained according to the image transformation model. This method effectively overcomes the problem that the threshold of the RANSAC algorithm needs to be manually adjusted when learning different models, thus reducing the parameter sensitivity of the algorithm, and improving the image stitching accuracy. Experiments show that our method is superior to existing methods both qualitatively and quantitatively.

Cite this article

ZHONG Mengfan , PEI Jihong . Image stitching enhancement method with symmetrical constraint of local feature points[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(S1) : 726948 -726948 . DOI: 10.7527/S1000-6893.2022.26948

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