Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Martin Huska"'
Publikováno v:
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision, 2022
Journal of Mathematical Imaging and Vision, 2022
Technologies for 3D data acquisition and 3D printing have enormously developed in the past few years, and, consequently, the demand for 3D virtual twins of the original scanned objects has increased. In this context, feature-aware denoising, hole fil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::482cd7893e89a27b9a9f92334582d590
https://doi.org/10.1007/s10851-022-01107-w
https://doi.org/10.1007/s10851-022-01107-w
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031319747
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7090fdfad24198e72d12cc39228451f3
https://doi.org/10.1007/978-3-031-31975-4_10
https://doi.org/10.1007/978-3-031-31975-4_10
Publikováno v:
Applied Mathematics and Computation. 451:128031
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60f455e3b58b476bb800f0e885264338
https://doi.org/10.1016/j.amc.2023.128031
https://doi.org/10.1016/j.amc.2023.128031
Publikováno v:
SIAM Journal on Imaging Sciences. 14:1749-1789
We propose a nonconvex variational decomposition model which separates a given image into piecewise-constant, smooth, and oscillatory components. This decomposition is motivated not only by image denoising and structure separation, but also by shadow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9913cf860b19d4664d03f69ff184582d
https://doi.org/10.1137/20m1355987
https://doi.org/10.1137/20m1355987
We propose a two stages signal decomposition method which efficiently separates a given signal into Jump, Oscillation and Trend. While there have been numerous advances in signal processing in past few decades, they mainly aim to analyze the signal i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa91cb961890c86b9ed52637c440c03b
http://hdl.handle.net/11697/176652
http://hdl.handle.net/11697/176652
We present a method for the generation of a pure quad mesh approximating a discrete manifold of arbitrary topology that preserves the patch layout characterizing the intrinsic object structure. A three-step procedure constitutes the core of our appro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa97a41cf305b7b45aef1d0327554bcd
http://hdl.handle.net/11585/831907
http://hdl.handle.net/11585/831907
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030755485
SSVM
SSVM
We propose a variational method for recovering discrete surfaces from noisy observations which promotes sparsity in the normal variation more accurately than \(\ell _1\) norm (total variation) and \(\ell _0\) pseudo-norm regularization methods by inc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4967caee5854ddc405d524ec4f0d0385
https://doi.org/10.1007/978-3-030-75549-2_35
https://doi.org/10.1007/978-3-030-75549-2_35
Publikováno v:
Springer Proceedings in Mathematics & Statistics ISBN: 9789811627002
We consider constructing a surface from a given set of point cloud data. We explore two fast algorithms to minimize the weighted minimum surface energy in [Zhao, Osher, Merriman and Kang, Comp.Vision and Image Under., 80(3):295-319, 2000]. An approac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de97d9cbfbba3ec5402a88548b34426d
http://hdl.handle.net/11585/777991
http://hdl.handle.net/11585/777991
Publikováno v:
Computational Science and Its Applications – ICCSA 2021 ISBN: 9783030869694
ICCSA (3)
ICCSA (3)
Electrical Impedance Tomography (EIT) is known to be a nonlinear and ill-posed inverse problem. Conventional penalty-based regularization methods rely on the linearized model of the nonlinear forward operator. However, the linearized problem is only
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d08e64abe0c68034f05b252d72ef9410
https://doi.org/10.1007/978-3-030-86970-0_44
https://doi.org/10.1007/978-3-030-86970-0_44