تقرير
Scale-Free Image Keypoints Using Differentiable Persistent Homology
| العنوان: | Scale-Free Image Keypoints Using Differentiable Persistent Homology |
|---|---|
| المؤلفون: | Barbarani, Giovanni, Vaccarino, Francesco, Trivigno, Gabriele, Guerra, Marco, Berton, Gabriele, Masone, Carlo |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Mathematics - Algebraic Topology, 55N31, I.2.10 |
| الوصف: | 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. Comment: Accepted to ICML 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.01315 |
| رقم الأكسشن: | edsarx.2406.01315 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |