A GPU based implementation of Center-Surround Distribution Distance for feature extraction and matching

The release of general purpose GPU programming environments has garnered universal access to computing performance that was once only available to super-computers. The availability of such computational power has fostered the creation and re-deployment of algorithms, new and old, creating entirely new classes of applications. In this paper, a GPU implementation of the Center-Surround Distribution Distance (CSDD) algorithm for detecting features within images and video is presented. While an optimized CPU implementation requires anywhere from several seconds to tens of minutes to perform analysis of an image, the GPU based approach has the potential to improve upon this by up to 28X, with no loss in accuracy.