Region Feature Descriptor Adapted to High Affine Transformations
<|reference_start|>Region Feature Descriptor Adapted to High Affine Transformations: To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.<|reference_end|>
@article{zhang2024region,
    title={Region Feature Descriptor Adapted to High Affine Transformations},
    author={Shaojie Zhang, Yinghui Wang, Bin Nan, Wei Li, Jinlong Yang, Tao Yan,
  Yukai Wang, Liangyi Huang, Mingfeng Wang, and Ibragim R. Atadjanov},
    journal={arXiv preprint arXiv:2402.09724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09724},
    primaryClass={cs.CV}
}