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

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.

Three-dimensional model of normal human dermal tissue using serial tissue sections

“…These methods improved the accuracy of the data and produced data that were more representative of the real dermal porosity and pore diameter. The SIFT algorithm, recognized as an excellent feature alignment operator, can be harnessed to address these challenges ( Li et al, 2015 ; Erbing et al, 2023 ; Meng et al, 2023 ). Key parameters for evaluating image alignment efficacy include mean square error, mutual information, and structural similarity score.…”