High precision image matching using 4D optimal minimisation

We propose a global method to match pair of images using the similarity information. Using a generic similarity distance between pixel pairs, this method can match any kind of images (gray levels, RGB, IR) or more generally any pair of 2D matrix (like spectrogram or wavelet transformations). Our algorithms search the best matching map in a 4-dimensional space defined by the Cartesian product of two input images. Several parameters like topological cost functions or global minimum search method can be adapted, function of specific applications. One of the proposed search method is an original extension of dynamic programming in 4D space. Other methods like iterated global searching or simulated annealing are proposed and their performances are compared. Typical applications are 3D stereo reconstruction, optical flow and velocity field computing or (sub-)pixel texture stretching measurement