no code implementations • 21 Jul 2021 • Dominik Kloepfer, Angelica I. Aviles-Rivero, Daniel Heydecker
Firstly, we prove that, under some weak assumptions, vertex embeddings derived from random walks do indeed converge both in the single limit of the number of random walks $N \to \infty$ and in the double limit of both $N$ and the length of each random walk $L\to\infty$.
no code implementations • 10 Oct 2019 • Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.
no code implementations • 29 May 2018 • Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.
no code implementations • 8 Feb 2018 • Georg Maierhofer, Daniel Heydecker, Angelica I. Aviles-Rivero, Samar M. Alsaleh, Carola-Bibiane Schönlieb
This paper addresses the search for a fast and meaningful image segmentation in the context of $k$-means clustering.