Computing Optimised Parallel Speeded-Up Robust Features (P-SURF) on Multi-Core Processors

Abstract

This article presents a novel CPU-based parallel algorithm (P-SURF) that computes the Speeded-Up Robust Features (SURF), a local descriptor that is able to find point correspondences between images in spite of scaling and rotation. The algorithm presented here parallelises all the seven major steps found in the original serial computation. The task in each of the steps is decomposed and the fractions are assigned to running threads bound onto distinctive processors. The implementation of the algorithm was tested using randomly selected images in regard to performance, scalability and stability. The results showed that its performance on mid-level Intel Core Duo processors was comparable to that of some fast GPU-based SURF implementations. For example, on a testing system equipped with an Intel Core Duo P8600 at 2.4 GHz, P-SURF was able to extract and represent features from a 640 × 480 image at a rate of 33 frames per second. The experimental results also revealed that, instead of leaving the threads to the kernel for processor assignment, assigning hard processor affinity by the algorithm produced better performance and stability.