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

Erbing Yang, Fei Chen (), Meiqing Wang, Hang Cheng and Rong Liu
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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

Mathematics, 2023, vol. 11, issue 5, 1-20

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.

Keywords: image registration; keypoint match; depth map; SIFT (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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