Scale-Free Image Keypoints Using Differentiable Persistent Homology
Autor: | Barbarani, Giovanni, Vaccarino, Francesco, Trivigno, Gabriele, Guerra, Marco, Berton, Gabriele, Masone, Carlo |
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Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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 |
Databáze: | arXiv |
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