Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01315 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914820943511552 |
|---|---|
| author | Barbarani, Giovanni Vaccarino, Francesco Trivigno, Gabriele Guerra, Marco Berton, Gabriele Masone, Carlo |
| author_facet | Barbarani, Giovanni Vaccarino, Francesco Trivigno, Gabriele Guerra, Marco Berton, Gabriele Masone, Carlo |
| contents | In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scale dependency and lack flexibility. This paper introduces a novel approach that leverages Morse theory and persistent homology, powerful tools rooted in algebraic topology. We propose a novel loss function based on the recent introduction of a notion of subgradient in persistent homology, paving the way toward topological learning. Our detector, MorseDet, is the first topology-based learning model for feature detection, which achieves competitive performance in keypoint repeatability and introduces a principled and theoretically robust approach to the problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scale-Free Image Keypoints Using Differentiable Persistent Homology Barbarani, Giovanni Vaccarino, Francesco Trivigno, Gabriele Guerra, Marco Berton, Gabriele Masone, Carlo Computer Vision and Pattern Recognition Machine Learning Algebraic Topology 55N31 I.2.10 In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scale dependency and lack flexibility. This paper introduces a novel approach that leverages Morse theory and persistent homology, powerful tools rooted in algebraic topology. We propose a novel loss function based on the recent introduction of a notion of subgradient in persistent homology, paving the way toward topological learning. Our detector, MorseDet, is the first topology-based learning model for feature detection, which achieves competitive performance in keypoint repeatability and introduces a principled and theoretically robust approach to the problem. |
| title | Scale-Free Image Keypoints Using Differentiable Persistent Homology |
| topic | Computer Vision and Pattern Recognition Machine Learning Algebraic Topology 55N31 I.2.10 |
| url | https://arxiv.org/abs/2406.01315 |