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 make use of graphics processing units (GPU) provided by commodity hardware. The programmable capabilities of modern GPUs allow speeding up counterpart CPU algorithms. In the case of corner-detector algorithms, most steps are easily translated from CPU to GPU. However, there are challenges for mapping the feature selection step to the GPU parallel computational model. This paper presents a template for implementing corner-detector algorithms that run entirely on 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 presented to demonstrate the algorithms efficiency.