ORB Feature Matching Algorithm Based on Multi-Scale Feature Description Fusion and Feature Point Mapping Error Correction

Yao, Chengxian; Zhang, Haifeng; Zhu, Jia; Fan, Dazhao; Fang, Yu; Tang, Lisa

doi:10.1109/access.2023.3288594

Cited by 2 publications

(1 citation statement)

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“…Generally, a higher count of feature points correlates with more precise image matching. Nevertheless, a dense accumulation of feature points can be detrimental to subsequent feature description, potentially compromising the accuracy of image matching [22], [23]. This paper introduces an enhanced ORB algorithm, featuring an optimized method for extracting feature points.…”

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confidence: 99%

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Borman,

Harjoko,

2024

IJACSA

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Feature points obtained using traditional ORB methods often exhibit redundancy, uneven distribution, and lack scale invariance. This study enhances the traditional ORB algorithm by presenting an optimal technique for extracting feature points, thereby overcoming these challenges. Initially, the image is partitioned into several areas. The determination of the quantity of feature points to be extracted from each region takes into account both the overall number of feature points and the number of divisions that the image undergoes. This method tackles concerns related to the overlap and redundancy of feature points in the extraction process. To counteract the nonscale invariance issue in feature points obtained via the ORB method, a Gaussian pyramid is employed, and feature points are extracted at each level. Experimental findings demonstrate that our method successfully extracts feature points with greater uniformity and rationality, while preserving image matching accuracy. Specifically, our technique outperforms the traditional ORB algorithm by approximately 4% and the SURF algorithm by 2% in terms of matching performance. Additionally, the processing time of our proposed algorithm is three times faster than that of the SURF algorithm and twelve times faster than the SIFT algorithm.

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Section: )mentioning

confidence: 99%