Region Feature Descriptor Adapted to High Affine Transformations
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911783581646848 |
|---|---|
| author | Zhang, Shaojie Wang, Yinghui Nan, Bin Li, Wei Yang, Jinlong Yan, Tao Wang, Yukai Huang, Liangyi Wang, Mingfeng Atadjanov, Ibragim R. |
| author_facet | Zhang, Shaojie Wang, Yinghui Nan, Bin Li, Wei Yang, Jinlong Yan, Tao Wang, Yukai Huang, Liangyi Wang, Mingfeng Atadjanov, Ibragim R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Region Feature Descriptor Adapted to High Affine Transformations Zhang, Shaojie Wang, Yinghui Nan, Bin Li, Wei Yang, Jinlong Yan, Tao Wang, Yukai Huang, Liangyi Wang, Mingfeng Atadjanov, Ibragim R. Computer Vision and Pattern Recognition 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. |
| title | Region Feature Descriptor Adapted to High Affine Transformations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.09724 |