High resolution non-rigid dense matching based on optimized sampling

A high resolution dense matching algorithm is presented for non-rigid image feature matching in the paper. For high resolution non-rigid images, telephoto lens is helpful in capturing fine scale features like cloth fold, pigmentation and skin pores. It brings us serious image noises which are less texture and bokeh, respectively. In order to avoid mismatch and non-uniform matching, we propose an optimized sampling method based on Gibbs dense sampling considering both texture feature similarity and spatial consistency. In the processing, first we extract connected image patches by triangulation among confidence matched point sets. Then our sampling method is executed in each connected image patch. We propose a judgment for matching points on the image patch boundary. Markov Random Field (MRF) model formulates the problem of dense matching as a Bayes decision task. Experiments are design to demonstrate the effective and efficiency of our method with active skin image data.