FAST detector on many-core computers

Many-core architectures are becoming increasingly popular due to advances in open programming environments. Typical architectures are Graphics Processing Units (GPUs) and Homogenous Computing Fabrics, offering high computing parallelism in order to decrease execution time of computationally intensive algorithms, for example in Computer Vision. In this paper, a low complexity interest point detector has been written in OpenCL language and executed on different desktop and embedded computing platforms. The algorithm performs interest point detection in an image, around which there is enough distinctive information to enable further description. The algorithm has been optimized on the targeted platforms and performance and accuracy comparison has been performed.