PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913919479578624 |
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| author | Ovanger, Oscar Hauge, Ragnar Skauvold, Jacob Pyrcz, Michael J. Eidsvik, Jo |
| author_facet | Ovanger, Oscar Hauge, Ragnar Skauvold, Jacob Pyrcz, Michael J. Eidsvik, Jo |
| contents | This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measure and mathematical morphology, PointSSIM enables robust comparison across binary images of varying resolutions by transforming them into marked point pattern representations. The key features of the image, referred to as anchor points, are extracted from binary images by identifying locally adaptive maxima from the minimal distance transform. Image comparisons are then performed using a summary vector, capturing intensity, connectivity, complexity, and structural attributes. Results show that this approach provides an efficient and reliable method for image comparison, particularly suited to applications requiring structural analysis across different resolutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric Ovanger, Oscar Hauge, Ragnar Skauvold, Jacob Pyrcz, Michael J. Eidsvik, Jo Computer Vision and Pattern Recognition This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measure and mathematical morphology, PointSSIM enables robust comparison across binary images of varying resolutions by transforming them into marked point pattern representations. The key features of the image, referred to as anchor points, are extracted from binary images by identifying locally adaptive maxima from the minimal distance transform. Image comparisons are then performed using a summary vector, capturing intensity, connectivity, complexity, and structural attributes. Results show that this approach provides an efficient and reliable method for image comparison, particularly suited to applications requiring structural analysis across different resolutions. |
| title | PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23833 |