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Local Property of Depth Information in 3D Images and Its Application in Feature Matching

Author

Listed:
  • Erbing Yang

    (College of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China)

  • Fei Chen

    (College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China)

  • Meiqing Wang

    (College of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China)

  • Hang Cheng

    (College of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China)

  • Rong Liu

    (College of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China)

Abstract

In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth difference of a keypoint and its neighbor pixel before and after the camera movement approximates a constant. That means the depth difference of a keypoint and its neighbor pixel after normalization is invariant to the camera movement. Based on this property, all the depth differences of a keypoint and its neighbor pixels constitute a local depth-based feature, which can be used as a supplement of the traditional feature. We combine the local depth-based feature with the SIFT feature descriptor to form a new feature descriptor, and the experimental results show the feasibility and effectiveness of the new feature descriptor.

Suggested Citation

  • Erbing Yang & Fei Chen & Meiqing Wang & Hang Cheng & Rong Liu, 2023. "Local Property of Depth Information in 3D Images and Its Application in Feature Matching," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1154-:d:1080945
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