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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07687 |
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| _version_ | 1866914912962347008 |
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| author | Venkatrayappa, Darshan Montesinos, Philippe Diep, Daniel Magnier, Baptiste |
| author_facet | Venkatrayappa, Darshan Montesinos, Philippe Diep, Daniel Magnier, Baptiste |
| contents | This paper introduces the new and powerful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RSD-DOG : A New Image Descriptor based on Second Order Derivatives Venkatrayappa, Darshan Montesinos, Philippe Diep, Daniel Magnier, Baptiste Computer Vision and Pattern Recognition This paper introduces the new and powerful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC. |
| title | RSD-DOG : A New Image Descriptor based on Second Order Derivatives |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.07687 |