一种基于四叉树划分的改进ORB算法

doi:10.3969/j.issn.0255-8297.2022.02.009

Abstract

Abstract: The image feature points extracted by the original ORB algorithm often appear the phenomenon of "clustering and overlapping", and their distribution is relatively dense and lack of scale invariance, which easily leads to the problem of mismatching of image feature points. In order to solve this problem, this paper proposes an image feature point extraction algorithm based on quad-tree structure. First, the scale pyramid of the image is built, and then the quad-tree is used to divide the image, and the depth of the partition is limited. The FAST algorithm is employed to detect the feature points of the scaled image by multiple detection thresholds. Second, the ORB feature points will be extracted based on the division of the total sub-block and the total number of the feature points. And then the best feature points are obtained by taking the maximum inhibition method, and the feature points' descriptors are calculated with the help of the improved BRIEF algorithm. Finally the work of feature points matching will be realized. Experimental results show that compared with the original ORB algorithm, the uniformity of feature points extracted by the proposed algorithm in this paper is significantly improved. The number of redundant and overlapping feature points is reduced, and the extraction speed of feature points is improved by more than 30%.

Key words: ORB(oriented FAST and rotated BRIEF) algorithm, quad-tree, image pyramid, feature extraction, image matching

CLC Number:

TP391.4

NI Cui, WANG Peng, SUN Hao, LI Qian. An Improved ORB Algorithm Based on Quad-Tree Partition[J]. Journal of Applied Sciences, 2022, 40(2): 266-278.

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An Improved ORB Algorithm Based on Quad-Tree Partition

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