Fast Image Diffusion for Feature Detection and Description

Abstract

Abstract-In this paper, we introduce a new multiscale 2D feature detection and description method based on optimal O(1) bilateral filter feature (OBFF). Existing methods detect and describe features by analyzing the scale space generated by linear and nonlinear diffusion kernel function, like Gaussian scale space and anisotropic diffusion scale space. By using the anisotropic diffusion scale space, KAZE features achieve significant progress on the 2D feature detection by using the anisotropic diffusion scale space. It makes the blurring locally adaptive and retains better feature localization accuracy and distinctiveness than the SIFT method. Our method OBFF also generates the nonlinear scale space of image to detect the local feature. The optimal bilateral filter is advantage in object boundary preserving and antinoise ability and dramatically speed up feature detection in nonlinear scale space. We use the benchmark datasets to compare our method with state-of-the-art approaches.Index Terms-Bilateral filter, nonlinear scale space, feature detection, SIFT, binary descriptor.

Mining Intelligence and Knowledge Exploration

“…The time performance of AKAZE is better than SURF or SIFT, but not as efficient as FAST [7]. The extended Kalman filter based mono SLAM section of the proposed algorithm is mostly similar to [5].…”