2024/06/03 by Giovanni Barbarani, Francesco Vaccarino, Barbarani, Giovanni +9
Computer Science · Engineering · #Topological and Geometric Data Analysis #Image Retrieval and Classification Techniques #Image Processing Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2406.01315
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.