A Riemanian approach to blob detection in manifold-valued images
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Publication:1689221
DOI10.1007/978-3-319-68445-1_84zbMATH Open1420.94022arXiv1905.13653OpenAlexW2766409792MaRDI QIDQ1689221
Author name not available (Why is that?)
Publication date: 12 January 2018
Abstract: This paper is devoted to the problem of blob detection in manifold-valued images. Our solution is based on new definitions of blob response functions. We define the blob response functions by means of curvatures of an image graph, considered as a submanifold. We call the proposed framework Riemannian blob detection. We prove that our approach can be viewed as a generalization of the grayscale blob detection technique. An expression of the Riemannian blob response functions through the image Hessian is derived. We provide experiments for the case of vector-valued images on 2D surfaces: the proposed framework is tested on the task of chemical compounds classification.
Full work available at URL: https://arxiv.org/abs/1905.13653
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