Abstract
Scale-Invariant Feature Transform (SIFT)[1][2] has lately attracted attention in computer vision as a robust feature point detection algorithm which is invariant for scale, rotation and illumination change. However, its computational complexity is too high to apply on practical real-time applications. The iterated Gaussian blurred operations on images lead to long computational latency and high memory requirement. In addition, the gradient histogram based descriptor needs lots of calculation and costs about 60% of total calculation time on generating the descriptor. We propose a binary-based descriptor that uses the intensity difference between neighboring pixels. The binary descriptor has the advantage of lower computational complexity and memory usage. For the use of the classifying the object, the binary descriptor also have the acceptable accuracy compared with the traditional histogram based descriptor. The proposed binary descriptor has 7.07x speed up than the original SIFT descriptor. In addition, it has 4.73x speed up compared with original SIFT algorithm[2]. The hardware implementation of the SIFT algorithm with the proposed binary descriptor applies parallel computing on the stage of feature location to accelerate the computing time on detecting feature points. The final implementation uses about 493-K gate count with 90-nm CMOS technology, and offers 7600 feature points/frame for 1080p images at 30 frames/s at the clock rate of 100 MHz.