Phase-correlation guided area matching for realtime vision and video encoding
In computer vision and video encoding applications, one of the first and most important steps is to establish a pixel-to-pixel correspondence between two images of the same scene obtained at slightly different times or points of view. One of the most popular methods to find these correspondences, known as Area Matching, consists in performing a computationally intensive search for each pixel in the first image, around a neighborhood of the same pixel in the second image. In this work we propose a method which significantly reduces the search space to only a few candidates, and permits the implementation of real-time vision and video encoding algorithms which do not require specialized hardware such as GPUs or FPGAs. Theoretical and experimental support for this method is provided. Specifically, we present results from the application of the method to the realtime video compression and transmission, as well as the realtime estimation of dense optical flow and stereo disparity maps, where a basic implementation achieves up to 100 fps in a typical dual-core PC.
realtime vision video encoding optical flow phase correlation stereo vision