Abstract: ASIFT(affine-SIFT) is a fully affine invariant,and scale invariant image local feature extraction algorithm．It has a good result in image matching．But because of its high computational complexity,it cannot be applied to real-time processing．Thus GPU is used to accelerate ASIFT．Based on the analysis of running time of ASIFT,firstly SIFT was adapted to GPU,and then the other parts of ASIFT．Memory pool was used in GASIFT to avoid frequently allocating and deleting memory during the runtime．Different ways of CPU/GPU synergetic parallel computing were studied to make GASIFT more efficient．Experiments show that the model in which CPU takes the logical calculation work and GPU makes parallel computing is the most suitable way．Based on this model,GASIFT has a good speed-up ratio over other methods．That’s 16times compared with traditional ASIFT,and 7times compared with OpenMP optimized ASIFT.

Key words: Image feature extraction,ASIFT,SIFT,CPU/GPU synergetic parallel