Description

Parallel computing targets problems that are scalable and possibly distributed, dividing the problem into smaller pieces. This approach may be explored to satisfy real time constraints required by augmented reality algoritms. The implementation is able to provide satisfactory speed up improvements using CUDA, NVIDIA’s architecture for GPU programming. The aim of this paper is not to present a new technology, but to show the great improvements that can be obtained by applying it in computer vision and augmented reality applications. The MP-KDD algorithm largely reduces the computational overhead by removing all floating-point and multiplication operations while preserving the currently popular SIFT and SURF algorithm essence. The MP-KDD algorithm can bedirectly and effectively mapped onto the pixel-parallel and row-parallel array processors of the vision chip. The vision chip architecture is also enhanced to realize direct memory access (DMA) and random access to array processors so that the MP-KDD algorithm can be executed more effectively.