Scale invariant feature transform(SIFT) is an algorithm to extract distinctive and invariant features from images to achieve reliable object matching between different images in variant scales and rotations. However, SIFT's huge computation impedes its real-time implementation. In this paper, a Fast-Gaussian SIFT(FG-SIFT) is proposed. Keypoint detection is optimized in FG-SIFT. SIFT's 2-D difference of Gaussian(DoG) in Gaussian Scale-Space(GSS) is separated into two 1-D DoG in x and y dimensions, and the level of scales in DoG pyramid is also reduced. The experiment shows that FG-SIFT reduces the computational complexity about 95% in GSS construction, also increases the accuracy of keypoint detection. Subsequently, the accuracy of generated features is increased 162%, and the accuracy of matched features is increased 8%. Apart from optimization in algorithm level, a hardware architecture of FG-SIFT's keypoint detection module is proposed. With a parallel architectural incorporating a five-stage pipeline, the execution time of keypoint detection is only 1.42ms@Xilinx Virtex5. Compared to conventional works, the speed is 21% faster than the fastest solution [9](ASIC), and hardware resources are 70% less than the most resources saved solutions [5] [6](Xilinx Virtex5).
['Liu Ke', 'Jun Wang', 'Xijun Zhao', 'Fan Liang']
Fast-Gaussian SIFT and its hardware architecture for keypoint detection