Author:
Cui Yuxia,Sun Zhipeng,Wang Xianlun
Abstract
A new feature description method based on the fusion of fast retina keypoint (FREAK) and the rotation-aware binary robust independent elementary features (rRBRIEF) is proposed to realize the effective combination of efficiency and accuracy of the two feature descriptions. In addition, in the elimination stage of mismatched point pairs, by setting the base point and its neighborhood, an improved neighborhood parallel random sample consensus (RANSAC) algorithm is proposed to achieve efficient parallel operation of the algorithm in multiple local neighborhoods. The improved feature point matching algorithm and the existing algorithm were tested in different scales, different rotations, different illuminations, and different fuzzy data sets. The experimental results show that the improved algorithm improves the average scene recognition accuracy by 18.21%, improves the efficiency by 15.58%, and shows good robustness.
Cited by
1 articles.
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1. Complex Scene Loading Optimization Based On Virtual Reality Algorithm;2023 4th International Conference for Emerging Technology (INCET);2023-05-26