Scale-Free Image Keypoints Using Differentiable Persistent Homology
<|reference_start|>Scale-Free Image Keypoints Using Differentiable Persistent Homology: 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.<|reference_end|>
@article{barbarani2024scale-free,
    title={Scale-Free Image Keypoints Using Differentiable Persistent Homology},
    author={Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco
  Guerra, Gabriele Berton, Carlo Masone},
    journal={arXiv preprint arXiv:2406.01315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01315},
    primaryClass={cs.CV cs.LG math.AT}
}