Affine invariant feature detection based on scale space analysis and depth information
基于深度信息与尺度空间的仿射不变特征检测*

This article presented an affine region detector using the depth information of the image. Is was concerned with a method for automatically detecting distinctive image patches covering identical physical part of scene even when the change of viewpoint. It could provide robust feature detection for further recognition process and could be widely used in the field of computer vision. The method was based on scale-space theory, which was also used for automatic scale selection. It proposed an algorithm, which could estimate the 3D part of the object and generate corresponding affine invariant Gaussian scale space with the depth information. This improved the reliability of feature detection, since the perspective transformation from 3D to 2D could be relative precisely simulated, which could compensate for the distortions due to perspective transformation. In order to test the robustness of the algorithm, experiments on real and synthetic images with known orientation were taken. The comparison with other affine invariant detectors was demonstrated as result.