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pro vyhledávání: '"Reininghaus, Jan"'
Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we est
Externí odkaz:
http://arxiv.org/abs/1412.6821
Autor:
Kasten, Jens, Reininghaus, Jan, Hotz, Ingrid, Hege, Hans-Christian, Noack, Bernd R., Daviller, Guillaume, Morzynski, Marek
In this paper, we propose a novel framework to extract features such as vortex cores and saddle points in two-dimensional unsteady flows. This feature extraction strategy generalizes critical points of snapshot topology in a Galilean-invariant manner
Externí odkaz:
http://arxiv.org/abs/1401.2462
Persistent homology is a popular and powerful tool for capturing topological features of data. Advances in algorithms for computing persistent homology have reduced the computation time drastically -- as long as the algorithm does not exhaust the ava
Externí odkaz:
http://arxiv.org/abs/1310.0710
We present a parallelizable algorithm for computing the persistent homology of a filtered chain complex. Our approach differs from the commonly used reduction algorithm by first computing persistence pairs within local chunks, then simplifying the un
Externí odkaz:
http://arxiv.org/abs/1303.0477
This paper proposes an efficient probabilistic method that computes combinatorial gradient fields for two dimensional image data. In contrast to existing algorithms, this approach yields a geometric Morse-Smale complex that converges almost surely to
Externí odkaz:
http://arxiv.org/abs/1208.6523
Akademický článek
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Autor:
Reininghaus, Jan
I propose a purely combinatorial framework that allows to extract the extremal structure of scalar and vector fields defined on discrete manifolds. The extremal structure of a scalar field consists of critical points and separatrices - certain tangen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4732::915d0c9c69075793b5d3ea5051afda0b
https://doi.org/10.17169/refubium-10396
https://doi.org/10.17169/refubium-10396
Autor:
Boyer, Edmond, Bronstein, Alexander M., Bronstein, Michael M., Bustos, Benjamin, Darom, Tal, Horaud, Radu, Hotz, Ingrid, Keller, Yosi, Keustermans, Johannes, Kovnatsky, Artiom, Litman, Roee, Reininghaus, Jan, Sipiran, Ivan, Smeets, Dirk, Suetens, Paul, Vandermeulen, Dirk, Zaharescu, Andrei, Zobel, Valentin
Publikováno v:
3DOR2011-Eurographics Workshop on 3D Object Retrieval
3DOR2011-Eurographics Workshop on 3D Object Retrieval, ACM Siggraph, Apr 2011, Llandudno, United Kingdom. pp.71-78, ⟨10.2312/3DOR/3DOR11/071-078⟩
3DOR2011-Eurographics Workshop on 3D Object Retrieval, ACM Siggraph, Apr 2011, Llandudno, United Kingdom. pp.71-78, ⟨10.2312/3DOR/3DOR11/071-078⟩
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the featur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac16fb7fbf33397fa8c303a9904cc1cc
In this work we propose a generalization of the Heat Kernel Signature (HKS). The HKS is a point signature derived from the heat kernel of the Laplace-Beltrami operator of a surface. In the theory of exterior calculus on a Riemannian manifold, the Lap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______8936::3efb113ff1d04e83a8e4d676921ddf05
http://hdl.handle.net/11025/1251
http://hdl.handle.net/11025/1251