A new image registration algorithm with robust cost function and progressive refinement estimation is developed on the basis of direct method (DM). The robustness lies in M-estimation to avert larger local noise and outliers. Moreover, the progressive refinement model estimation under the multi-resolution framework, where the initial parameter values of coarse level are estimated by Mellin transformation, is adopted so as to get global optimization and reduce search space. Experiments show that the proposed algorithm greatly extends the scope of the conventional DM of image registration.

This letter presents a novel application of iterated function system (IFS) based three-dimensional (3D) fractal interpolation to compression elevation data. The parameters of contractive transformations are simplified by a concise fractal iteration form with geometric meaning. A local iteration algorithm is proposed, which can solve the non-separation problem when Collage Theorem is applied to find the appropriate fractal parameters. The elevation data compression is proved experimentally to be effective in reconstruction quality and time-saving.

In this letter, it is shown that there exists relationship between phase-based and area-based stereo in spite of their different motivations. A new cost function is defined based on this clue and an improved cost-minimization framework is presented. It is suitable for disparity estimation and occlusion detection in aerial scenes.