Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape reconstruction from cast shadows using coplanarities and metric constraints
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
SIFT, short for Scale Invariant Feature Transform, is regarded as one of the most robust feature detection algorithms The Gaussian and DoG Pyramid Construction part, functioning as computation basis and searching spaces for other parts, proves fatal to the system In this paper, we present an FPGA-implementable hardware accelerator for this part Stratified Gaussian Convolution scheme and 7-Round Parallel Computation scheme are introduced to reduce the hardware cost and improve process speed, meanwhile keeping high accuracy In our experiment, our proposal successfully realizes a system with max clock frequency of 95.0 MHz, and on-system process speed of up to 21 fps for VGA format images Hardware cost of Slice LUTs is reduced by 12.1% compared with traditional work Accuracy is kept as high as 98.27% against original software solution Our proposed structure proves to be suitable for real-time SIFT systems.