GPU acceleration of feature extraction and matching algorithms

During the last years the applications of Computer Vision have increased greatly in many different contexts, owing to the availability of more and more powerful hardware. However, in some situations, the problem of algorithms with a high computational time still continues to limit their growth. One of the causes is that the progress from the point of view of software was much lower, despite very efficient algorithms have been discovered. This paper is focused on a way to accelerate some computer vision algorithms. In particular, they will be described and tested the benefits of running on a Graphical Processing Unit (GPU) the Feature Group Matching (FGM) algorithm, a novel approach to local feature matching to select stable features and obtain a more reliable similarity value between two images. Being FGM based on the state of the art algorithms Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), also the performances of these algorithms on a GPU implementation using the Compute Unified Device Architecture (CUDA) will be described.