Anti-Viewpoint Changing Image Matching Algorithm Based on SURF

Ke-xin, 苏可心 Su; Guang-liang, 韩广良 Han; Hai-jiang, 孙海江 Sun

doi:10.3788/yjyxs20132804.0626

Cited by 1 publication

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“…In view of this, Morel et al [9] proposed the ASIFT (Affine-SIFT) algorithm with anti-viewpoint transformation capability by simulating the viewpoint deformation using affine sampling. Based on the ASIF algorithm, the ASURF [10] (Affine-SURF) algorithm, the AORB [11] (Affine-ORB) method, and the AFREAK algorithm [12] (Affine-FREAK) utilize the SURF, ORB, and FREAK algorithm instead of SIFT algorithm, which effectively improves the computational speed of the algorithm. Cai et al proposed PSIFT (Perspective-SIFT) algorithm [13] based on ASIFT algorithm using the perspective transformation model instead of the affine transformation model, which improves the matching accuracy.…”

Section: Introductionmentioning

confidence: 99%

A feature extraction and matching method in large parallax scenes

周¹,

田²

2023

SPIE-CLP Conference on Advanced Photonics 2022

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A feature point extraction and matching algorithm based on affine transformation space is proposed to address the shortcomings of existing feature extraction and matching algorithms in large view scenes with few effective matching points and slow matching speed. The algorithm first constructs the affine change space to simulate the viewpoint change and obtains the affine invariance; then avoids the feature point detection in the invalid region by dividing the valid region; in the feature description stage, the ORB algorithm is incorporated into the affine change space, while the gradient contrast information of multiple directions in the feature point sampling region is fused to obtain the final binary descriptor. Through experiments on large-view datasets and sequence images, it is demonstrated that the algorithm has better matching effect in large-view scenes, and also has more advantages in time efficiency.

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