SMCEWS: Binary Robust Multicentre Features

Abstract

The Oriented FAST and Rotated BRIEF (ORB) algorithms have been improved, and a new method for calculating descriptors based on symmetrical multicentric weighted binary encoding is proposed, which enhances the robustness of feature points. This method employs a string of binary descriptors to encode the features and uses the multiple descriptor centre strategy to sample descriptors at the feature point and on the symmetrical circumference around it. Furthermore, a weighted summation is introduced in the descriptor calculation process to address the noise in the image during the sampling process. Specifically, the pixel values around the sampled point and the sampled point itself are combined using a certain weight to produce the final pixel value of the sampled point. The reliability of the descriptor is enhanced by introducing the pixel information around the sample point while solving the noise problem. Our method makes full use of the pixel information in the various parts of the descriptor sampling region to improve the distinguishability of the descriptors. We compare it with the ORB algorithm and experimentally show that the feature extraction method achieves better matching results with almost constant computation time.

Keywords

• ORB
• symmetrical multicentric weighted
• binary descriptors
• feature extraction