An invariant interest point detector under image affine transformation

LIN Rui(林睿)¹, HUANG Hai-bo(黄海波)^{1, 2}, SUN Rong-chuan(孙荣川)¹, SUN Li-ning(孙立宁)¹

(1. School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215006, China;
2. College of Mechanical and Electrical Engineering & Collaborative Innovation Center of
Suzhou Nano Science and Technology, Soochow University, Suzhou 215123, China)

Abstract:For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor (PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.

Key words:local orientation tensor; interest point detector; affine invariant; image polynomial expansion