Abstract

Harris corner detector is a classic tool to extract feature. It is stable to illumination change and rotation but unstable to more complicated transform. In order to register images with different viewpoints, we extend Harris corner detector to scale-space to gain invariance to scale change, then we apply affine shape adaptation to the scale invariant point until convergence is reached, giving it invariance to affine transform. With these local features, we use general feature descriptor and matching algorithm to generate matches and then use the matches to calculate the geometric transform matrix, which enables the final registration. Result shows that our algorithm can get more accurate matches than scale invariant feature transform SIFT, and less difference exists between registered images.

© 2010 Chinese Optics Letters

Image registration method for multimodal images

Wang Bingjian, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, and Lai Rui
Appl. Opt. 50(13) 1861-1867 (2011)

Remote sensing image registration approach based on a retrofitted SIFT algorithm and Lissajous-curve trajectories

Zhi-li Song, Sheng Li, and Thomas F. George
Opt. Express 18(2) 513-522 (2010)

Multi-sensor image registration based on algebraic projective invariants

Bin Li, Wei Wang, and Hao Ye
Opt. Express 21(8) 9824-9838 (2013)

Invariant matching method for different viewpoint angle images

Min Chen, Zhenfeng Shao, Dongyang Li, and Jun Liu
Appl. Opt. 52(1) 96-104 (2013)

Feature matching based on curve descriptor and local D-Nets for fish-eye images

Yakun Zhang, Hongsheng Zhang, and Wenming Zhang
J. Opt. Soc. Am. A 37(5) 787-796 (2020)