Systematic review of image segmentation using complex networks
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909062966280192 |
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| author | Rezaei, Amin Asadi, Fatemeh |
| author_facet | Rezaei, Amin Asadi, Fatemeh |
| contents | This review presents various image segmentation methods using complex networks.
Image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation.
In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Systematic review of image segmentation using complex networks Rezaei, Amin Asadi, Fatemeh Computer Vision and Pattern Recognition This review presents various image segmentation methods using complex networks. Image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation. In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks. |
| title | Systematic review of image segmentation using complex networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.02758 |