FPGA-based detection of SIFT interest keypoints
The use of local features in images has become very popular due to its promising results. They have shown significant benefits in a variety of applications such as object recognition, image retrieval, robot navigation, panorama stitching, and others. SIFT is one of the local features methods that have shown better results. Among its main disadvantages is its high computational cost. In order to speedup this algorithm, this work proposes the design and implementation of an efficient hardware architecture based on FPGAs for SIFT interest point detection In order to take full advantage of the parallelism in this algorithm and to minimize the device area occupied by its implementation in hardware, part of the algorithm was reformulated. The main contribution of the hardware architecture proposed in this paper and the main difference with the rest of the architectures reported in the literature is that as the number of octaves to be processed is increased, the amount of occupied device area remains almost constant. The evaluations and experiments to the architecture support this contribution, as well as accuracy, repeatability, and distinctiveness of the results. Experiments also showed device area occupation and time constraints of the hardware implementation. The architecture presented in this paper is able to detect interest points in an image of 320 x 240 in 11 ms, which represents a speedup of 250 x with respect to a software implementation.
fpga sift local features hardware architecture keypoint detection