An Efficient and Geometric-Distortion-Free Binary Robust Local Feature - Jing-Ming Guo; Li-Ying Chang; Jiann-Der Lee (Sensors, 2019)
An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency.