Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Bogdan, Savchynskyy"'
Autor:
Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter, Florian Bernard, Carsten Rother, Dagmar Kainmüller, Paul Swoboda, Bogdan Savchynskyy
Publikováno v:
Computer Vision--ECCV 2022
Lecture Notes in Computer Science
Lecture Notes in Computer Science ISBN: 9783031200496
Lecture Notes in Computer Science
Lecture Notes in Computer Science ISBN: 9783031200496
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::890286fb40a5760baf2fb03ecf347b5a
http://arxiv.org/abs/2207.00291
http://arxiv.org/abs/2207.00291
Autor:
Lisa Hutschenreiter, Stefan Haller, Lorenz Feineis, Carsten Rother, Dagmar Kainmuller, Bogdan Savchynskyy
We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::227fc8f64c18a00c3a13cf25813dcd2a
http://arxiv.org/abs/2101.12085
http://arxiv.org/abs/2101.12085
Autor:
Bogdan Savchynskyy
This monograph is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem. Modeling, appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90d457488fea79413d94d0343e3d4f10
Autor:
Anurag Arnab, Philip H. S. Torr, Carsten Rother, Bernardino Romera-Paredes, Måns Larsson, Alexander Kirillov, Shuai Zheng, Fredrik Kahl, Sadeep Jayasumana, Bogdan Savchynskyy
Publikováno v:
IEEE Signal Processing Magazine
Semantic Segmentation is the task of labelling every pixel in an image with a pre-defined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical
Autor:
Bogdan Savchynskyy
Discrete Graphical Models — An Optimization Perspective is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combina
Publikováno v:
Computer Graphics Forum. 36:197-208
Various processing algorithms on point set surfaces rely on consistently oriented normals e.g. Poisson surface reconstruction. While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determi
Autor:
Alexander Kirillov, Dmitrij Schlesinger, Philip H. S. Torr, Bogdan Savchynskyy, Shuai Zheng, Carsten Rother
Publikováno v:
Computer Vision – ACCV 2016 ISBN: 9783319541839
ACCV (2)
ACCV (2)
We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a stan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f7becbf56353ea6003d54ce82c18366
https://ora.ox.ac.uk/objects/uuid:ef5c8eae-6e03-41f4-bd0c-5e5b934f97eb
https://ora.ox.ac.uk/objects/uuid:ef5c8eae-6e03-41f4-bd0c-5e5b934f97eb
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012243
ECCV (4)
ECCV (4)
Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coord
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e73c923843769102d401bebeeda0cf8f
https://doi.org/10.1007/978-3-030-01225-0_16
https://doi.org/10.1007/978-3-030-01225-0_16
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 40(7)
We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of t