Gradient-based registration of rotated, scaled, and translated images

Xiangguo Li

doi:10.1117/12.2031607

27 October 2013 Gradient-based registration of rotated, scaled, and translated images

Xiangguo Li

Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891917 (2013) https://doi.org/10.1117/12.2031607
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

The paper presents a gradient-based algorithm for image registration. The algorithm is extended from the classical Lucas-Kanade algorithm, and it aims to solve the rotation-scale-translation (RST) model. To solve the problem, the 6- parameter affine model is used, and the algorithm is derived according to the idea of the Lucas-Kanade algorithm, then the RST model parameters are obtained from the estimated affine model values. Due to its Taylor approximation nature, iterative scheme is needed, and the inverse compositional scheme by Keren et al. is used. To further increase the speed and convergence range, coarse-to-fine strategy is also used. In the final, simulations are performed to verify and evaluate the algorithm, and the results demonstrate that it can obtain sub-pixel estimation with high accuracy.

Citation Download Citation

Xiangguo Li "Gradient-based registration of rotated, scaled, and translated images", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891917 (27 October 2013); https://doi.org/10.1117/12.2031607

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