Accelerated Corner Detector Algorithms

Fast corner detector algorithms are important for achieving real time in different computer vision applications. In this paper, we present new algorithm implementations for corner detection that makes use of the graphics processing units (GPU) of commodity hardware. The programmable capabilities of modern GPUs make it possible to speed up counterpart CPU algorithms. For corner detector algorithms, most steps are easily translated from CPU to GPU. However, the feature selection step imposes challenges for being mapped to the GPU parallel computational model. This paper presents a template for implementing corner detector algorithms that run entirely on the GPU, resulting in significant speed-ups. The proposed template is used to implement the KLT corner detector and the Harris corner detector, and numerical results are shown to demonstrate the algorithm efficiency.