Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916453973753856 |
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| author | Huang, Yuan De Bortoli, Valentin Zhou, Fugen Gilles, Jerome |
| author_facet | Huang, Yuan De Bortoli, Valentin Zhou, Fugen Gilles, Jerome |
| contents | Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. In order to focus only on the textural information, we also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19191 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets Huang, Yuan De Bortoli, Valentin Zhou, Fugen Gilles, Jerome Computer Vision and Pattern Recognition 42C40 G.0 Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. In order to focus only on the textural information, we also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images. |
| title | Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets |
| topic | Computer Vision and Pattern Recognition 42C40 G.0 |
| url | https://arxiv.org/abs/2410.19191 |