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.
We present a practical hexagonal storage and addressing scheme, which eliminates the difference between theory and implementation in other addressing methods. This scheme employs a middleware-based address-mapping module that separates the algorithm and specific data addressing; thus any hexagonal algorithm can keep the native and consistent forms as in the coordinate system through theory and implementation. The scheme simplifies the implementation work and preserves all excellent features of the hexagonal lattice. Finally, we discuss the implementation issues and show that it’s feasible and can be implemented efficiently.