An Improved ORB Algorithm Based on Multi-Feature Fusion
Chaoqun Ma, Xiaoguang Hu, Li Hua Fu, Guofeng Zhang · 2018
In order to improve the matching accuracy and reduce mismatch of the ORB algorithm when it was directly applied into image matching, an improved ORB algorithm is proposed based on the LATCH feature and the improved LBP feature. In feature point detection, the multi-scale FAST corner detection algorithm is used to find out the feature point with scale invariance. In feature point description, the LATCH feature and the LBP feature are used to generate feature point descriptor. Aiming at the problem that the LBP feature is too sensitive to noise and image local change, image block method is applied into the generate the improved LBP feature proposed in this paper. Then these two features are combined to form a new binary LATCH/LBP feature by multi-feature fusion method. Then combined with the Center of gravity method used in the ORB algorithm, the new LATCH/LBP feature can obtain strong robustness. In feature matching, the nearest neighbor matching algorithm is used to realize the matching between two image feature vectors. Finally, the matching results are screened by RANSAC algorithm to eliminate mismatch points. The experiment results show that the improved ORB algorithm proposed in this paper can effectively improve the matching accuracy of the ORB algorithm on the basis of real-time performance.