---
_id: '14448'
abstract:
- lang: eng
text: We consider the problem of solving LP relaxations of MAP-MRF inference problems,
and in particular the method proposed recently in [16], [35]. As a key computational
subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth
convex function over a combinatorial polytope. We propose an efficient implementation
of this subroutine based on in-face Frank-Wolfe directions, introduced in [4]
in a different context. More generally, we define an abstract data structure for
a combinatorial subproblem that enables in-face FW directions, and describe its
specialization for tree-structured MAP-MRF inference subproblems. Experimental
results indicate that the resulting method is the current state-of-art LP solver
for some classes of problems. Our code is available at pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html.
article_processing_charge: No
arxiv: 1
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Kolmogorov V. Solving relaxations of MAP-MRF problems: Combinatorial in-face
Frank-Wolfe directions. In: Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition. Vol 2023. IEEE; 2023:11980-11989.
doi:10.1109/CVPR52729.2023.01153'
apa: 'Kolmogorov, V. (2023). Solving relaxations of MAP-MRF problems: Combinatorial
in-face Frank-Wolfe directions. In Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (Vol. 2023, pp. 11980–11989).
Vancouver, Canada: IEEE. https://doi.org/10.1109/CVPR52729.2023.01153'
chicago: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial
in-Face Frank-Wolfe Directions.” In Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, 2023:11980–89. IEEE,
2023. https://doi.org/10.1109/CVPR52729.2023.01153.'
ieee: 'V. Kolmogorov, “Solving relaxations of MAP-MRF problems: Combinatorial in-face
Frank-Wolfe directions,” in Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, vol.
2023, pp. 11980–11989.'
ista: 'Kolmogorov V. 2023. Solving relaxations of MAP-MRF problems: Combinatorial
in-face Frank-Wolfe directions. Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition vol. 2023, 11980–11989.'
mla: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial
in-Face Frank-Wolfe Directions.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2023, IEEE, 2023, pp. 11980–89,
doi:10.1109/CVPR52729.2023.01153.'
short: V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, IEEE, 2023, pp. 11980–11989.
conference:
end_date: 2023-06-24
location: Vancouver, Canada
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2023-06-17
corr_author: '1'
date_created: 2023-10-22T22:01:16Z
date_published: 2023-08-22T00:00:00Z
date_updated: 2025-09-09T13:09:58Z
day: '22'
department:
- _id: VlKo
doi: 10.1109/CVPR52729.2023.01153
external_id:
arxiv:
- '2010.09567'
isi:
- '001062522104029'
intvolume: ' 2023'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2010.09567'
month: '08'
oa: 1
oa_version: Preprint
page: 11980-11989
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9798350301298'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe
directions'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2023
year: '2023'
...
---
_id: '14114'
abstract:
- lang: eng
text: Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with increased
degradation on the best performing groups). Extending the bias-variance decomposition
for classification to fairness, we theoretically explain why the majority of fairness
methods designed for low capacity models should not be used in settings involving
high-capacity models, a scenario common to computer vision. We corroborate this
analysis with extensive experimental support that shows that many of the fairness
heuristics used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation strategy
that, uniquely, of all methods tested, improves performance for the disadvantaged
groups.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Guha
full_name: Balakrishnan, Guha
last_name: Balakrishnan
- first_name: Matthaus
full_name: Kleindessner, Matthaus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision:
Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics
Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016'
apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F.,
Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto
inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016'
chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner,
Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in
Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 10400–411. Institute
of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.'
ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies
in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.'
ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf
B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in
fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.'
mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies
in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022,
pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.'
short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B.
Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-08-21T12:18:00Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-11T09:19:14Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/cvpr52688.2022.01016
extern: '1'
external_id:
arxiv:
- '2203.04913'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04913
month: '07'
oa: 1
oa_version: Preprint
page: 10400-10411
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781665469470'
issn:
- 1063-6919
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '9957'
abstract:
- lang: eng
text: The reflectance field of a face describes the reflectance properties responsible
for complex lighting effects including diffuse, specular, inter-reflection and
self shadowing. Most existing methods for estimating the face reflectance from
a monocular image assume faces to be diffuse with very few approaches adding a
specular component. This still leaves out important perceptual aspects of reflectance
as higher-order global illumination effects and self-shadowing are not modeled.
We present a new neural representation for face reflectance where we can estimate
all components of the reflectance responsible for the final appearance from a
single monocular image. Instead of modeling each component of the reflectance
separately using parametric models, our neural representation allows us to generate
a basis set of faces in a geometric deformation-invariant space, parameterized
by the input light direction, viewpoint and face geometry. We learn to reconstruct
this reflectance field of a face just from a monocular image, which can be used
to render the face from any viewpoint in any light condition. Our method is trained
on a light-stage training dataset, which captures 300 people illuminated with
150 light conditions from 8 viewpoints. We show that our method outperforms existing
monocular reflectance reconstruction methods, in terms of photorealism due to
better capturing of physical premitives, such as sub-surface scattering, specularities,
self-shadows and other higher-order effects.
acknowledgement: "We thank Tarun Yenamandra and Duarte David for helping us with the
comparisons. This work was supported by the\r\nERC Consolidator Grant 4DReply (770784).
We also acknowledge support from InterDigital."
article_processing_charge: No
arxiv: 1
author:
- first_name: Mallikarjun
full_name: B R, Mallikarjun
last_name: B R
- first_name: Ayush
full_name: Tewari, Ayush
last_name: Tewari
- first_name: Tae-Hyun
full_name: Oh, Tae-Hyun
last_name: Oh
- first_name: Tim
full_name: Weyrich, Tim
last_name: Weyrich
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Hanspeter
full_name: Pfister, Hanspeter
last_name: Pfister
- first_name: Wojciech
full_name: Matusik, Wojciech
last_name: Matusik
- first_name: Mohamed
full_name: Elgharib, Mohamed
last_name: Elgharib
- first_name: Christian
full_name: Theobalt, Christian
last_name: Theobalt
citation:
ama: 'B R M, Tewari A, Oh T-H, et al. Monocular reconstruction of neural face reflectance
fields. In: Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition. IEEE; 2021:4791-4800. doi:10.1109/CVPR46437.2021.00476'
apa: 'B R, M., Tewari, A., Oh, T.-H., Weyrich, T., Bickel, B., Seidel, H.-P., …
Theobalt, C. (2021). Monocular reconstruction of neural face reflectance fields.
In Proceedings of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (pp. 4791–4800). Nashville, TN, United States; Virtual:
IEEE. https://doi.org/10.1109/CVPR46437.2021.00476'
chicago: B R, Mallikarjun, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel,
Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, and
Christian Theobalt. “Monocular Reconstruction of Neural Face Reflectance Fields.”
In Proceedings of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, 4791–4800. IEEE, 2021. https://doi.org/10.1109/CVPR46437.2021.00476.
ieee: M. B R et al., “Monocular reconstruction of neural face reflectance
fields,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Nashville, TN, United States; Virtual, 2021,
pp. 4791–4800.
ista: 'B R M, Tewari A, Oh T-H, Weyrich T, Bickel B, Seidel H-P, Pfister H, Matusik
W, Elgharib M, Theobalt C. 2021. Monocular reconstruction of neural face reflectance
fields. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
4791–4800.'
mla: B R, Mallikarjun, et al. “Monocular Reconstruction of Neural Face Reflectance
Fields.” Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, IEEE, 2021, pp. 4791–800, doi:10.1109/CVPR46437.2021.00476.
short: M. B R, A. Tewari, T.-H. Oh, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister,
W. Matusik, M. Elgharib, C. Theobalt, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2021, pp. 4791–4800.
conference:
end_date: 2021-06-25
location: Nashville, TN, United States; Virtual
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2021-06-20
date_created: 2021-08-24T06:03:00Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2023-08-11T11:08:35Z
day: '01'
ddc:
- '000'
department:
- _id: BeBi
doi: 10.1109/CVPR46437.2021.00476
external_id:
arxiv:
- '2008.10247'
isi:
- '000739917304096'
file:
- access_level: open_access
checksum: 961db0bde76dd87cf833930080bb9f38
content_type: application/pdf
creator: bbickel
date_created: 2021-08-24T06:02:15Z
date_updated: 2021-08-24T06:02:15Z
file_id: '9958'
file_name: R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper[1].pdf
file_size: 4746649
relation: main_file
file_date_updated: 2021-08-24T06:02:15Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Preprint
page: 4791-4800
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- 978-166544509-2
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Monocular reconstruction of neural face reflectance fields
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '7468'
abstract:
- lang: eng
text: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference
in structured energy minimization problems. The method optimizes a Lagrangean
relaxation of the original energy minimization problem using a multi plane block-coordinate
Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean
decomposition. We show empirically that our method outperforms state-of-the-art
Lagrangean decomposition based algorithms on some challenging Markov Random Field,
multi-label discrete tomography and graph matching problems.
article_number: 11138-11147
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. Vol 2019-June. IEEE; 2019. doi:10.1109/CVPR.2019.01140'
apa: 'Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate
Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA,
United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140'
chicago: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vol. 2019–June. IEEE, 2019. https://doi.org/10.1109/CVPR.2019.01140.
ieee: P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe
algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol.
2019–June.
ista: 'Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe
algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition
vol. 2019–June, 11138–11147.'
mla: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2019–June, 11138–11147, IEEE,
2019, doi:10.1109/CVPR.2019.01140.
short: P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2019.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2019-06-15
date_created: 2020-02-09T23:00:52Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2025-07-10T11:54:39Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/CVPR.2019.01140
ec_funded: 1
external_id:
arxiv:
- '1806.05049'
isi:
- '000542649304076'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.05049
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9781728132938'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Map inference via block-coordinate Frank-Wolfe algorithm
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2019-June
year: '2019'
...
---
_id: '18287'
abstract:
- lang: eng
text: Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms used
as a post-processing stage in the functional correspondence framework. Such frequently
used techniques implicitly make restrictive assumptions (e.g., nearisometry) on
the considered shapes and in practice suffer from lack of accuracy and result
in poor surjectivity. We propose an alternative recovery technique capable of
guaranteeing a bijective correspondence and producing significantly higher accuracy
and smoothness. Unlike other methods our approach does not depend on the assumption
that the analyzed shapes are isometric. We derive the proposed method from the
statistical framework of kernel density estimation and demonstrate its performance
on several challenging deformable 3D shape matching datasets.
article_processing_charge: No
arxiv: 1
author:
- first_name: Matthias
full_name: Vestner, Matthias
last_name: Vestner
- first_name: Roee
full_name: Litman, Roee
last_name: Litman
- first_name: Emanuele
full_name: Rodola, Emanuele
last_name: Rodola
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Daniel
full_name: Cremers, Daniel
last_name: Cremers
citation:
ama: 'Vestner M, Litman R, Rodola E, Bronstein AM, Cremers D. Product manifold filter:
Non-rigid shape correspondence via kernel density estimation in the product space.
In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
IEEE; 2017:6681-6690. doi:10.1109/cvpr.2017.707'
apa: 'Vestner, M., Litman, R., Rodola, E., Bronstein, A. M., & Cremers, D. (2017).
Product manifold filter: Non-rigid shape correspondence via kernel density estimation
in the product space. In 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) (pp. 6681–6690). Honolulu, HI, United States: IEEE. https://doi.org/10.1109/cvpr.2017.707'
chicago: 'Vestner, Matthias, Roee Litman, Emanuele Rodola, Alex M. Bronstein, and
Daniel Cremers. “Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel
Density Estimation in the Product Space.” In 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 6681–90. IEEE, 2017. https://doi.org/10.1109/cvpr.2017.707.'
ieee: 'M. Vestner, R. Litman, E. Rodola, A. M. Bronstein, and D. Cremers, “Product
manifold filter: Non-rigid shape correspondence via kernel density estimation
in the product space,” in 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Honolulu, HI, United States, 2017, pp. 6681–6690.'
ista: 'Vestner M, Litman R, Rodola E, Bronstein AM, Cremers D. 2017. Product manifold
filter: Non-rigid shape correspondence via kernel density estimation in the product
space. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
30th IEEE Conference on Computer Vision and Pattern Recognition, 6681–6690.'
mla: 'Vestner, Matthias, et al. “Product Manifold Filter: Non-Rigid Shape Correspondence
via Kernel Density Estimation in the Product Space.” 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp. 6681–90, doi:10.1109/cvpr.2017.707.'
short: M. Vestner, R. Litman, E. Rodola, A.M. Bronstein, D. Cremers, in:, 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp.
6681–6690.
conference:
end_date: 2017-07-26
location: Honolulu, HI, United States
name: 30th IEEE Conference on Computer Vision and Pattern Recognition
start_date: 2017-07-21
date_created: 2024-10-09T07:49:43Z
date_published: 2017-11-09T00:00:00Z
date_updated: 2024-12-05T14:20:16Z
day: '09'
doi: 10.1109/cvpr.2017.707
extern: '1'
external_id:
arxiv:
- '1701.00669'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1701.00669
month: '11'
oa: 1
oa_version: Preprint
page: 6681 - 6690
publication: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
isbn:
- '9781538604588'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Product manifold filter: Non-rigid shape correspondence via kernel density
estimation in the product space'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
OA_type: closed access
_id: '3700'
abstract:
- lang: eng
text: We propose a new method to partition an unlabeled dataset, called Discriminative
Context Partitioning (DCP). It is motivated by the idea of splitting the dataset
based only on how well the resulting parts can be separated from a context class
of disjoint data points. This is in contrast to typical clustering techniques
like K-means that are based on a generative model by implicitly or explicitly
searching for modes in the distribution of samples. The discriminative criterion
in DCP avoids the problems that density based methods have when the a priori assumption
of multimodality is violated, when the number of samples becomes small in relation
to the dimensionality of the feature space, or if the cluster sizes are strongly
unbalanced. We formulate DCP‘s separation
property as a large-margin criterion, and show how the resulting optimization
problem can be solved efficiently. Experiments on the MNIST and USPS datasets
of handwritten digits and on a subset of the Caltech256 dataset show that, given
a suitable context, DCP can achieve good results even in situation where density-based
clustering techniques fail.
acknowledgement: This work was funded in part by the EC project CLASS, IST 027978.
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Partitioning of image datasets using discriminative context information.
In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE;
2008:1-8. doi:10.1109/CVPR.2008.4587448'
apa: 'Lampert, C. (2008). Partitioning of image datasets using discriminative context
information. In 2008 IEEE Conference on Computer Vision and Pattern Recognition
(pp. 1–8). Anchorage, AK, United States: IEEE. https://doi.org/10.1109/CVPR.2008.4587448'
chicago: Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative
Context Information.” In 2008 IEEE Conference on Computer Vision and Pattern
Recognition, 1–8. IEEE, 2008. https://doi.org/10.1109/CVPR.2008.4587448.
ieee: C. Lampert, “Partitioning of image datasets using discriminative context information,”
in 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage,
AK, United States, 2008, pp. 1–8.
ista: 'Lampert C. 2008. Partitioning of image datasets using discriminative context
information. 2008 IEEE Conference on Computer Vision and Pattern Recognition.
CVPR: Computer Vision and Pattern Recognition, 1–8.'
mla: Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context
Information.” 2008 IEEE Conference on Computer Vision and Pattern Recognition,
IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587448.
short: C. Lampert, in:, 2008 IEEE Conference on Computer Vision and Pattern Recognition,
IEEE, 2008, pp. 1–8.
conference:
end_date: 2008-06-28
location: Anchorage, AK, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2008-06-23
date_created: 2018-12-11T12:04:41Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:40:17Z
day: '18'
doi: 10.1109/CVPR.2008.4587448
extern: '1'
language:
- iso: eng
main_file_link:
- url: http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
isbn:
- '9781424422425'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2657'
status: public
title: Partitioning of image datasets using discriminative context information
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...
---
_id: '3712'
abstract:
- lang: eng
text: We present a new method for spectral clustering with paired data based on
kernel canonical correlation analysis, called correlational spectral clustering.
Paired data are common in real world data sources, such as images with text captions.
Traditional spectral clustering algorithms either assume that data can be represented
by a single similarity measure, or by co-occurrence matrices that are then used
in biclustering. In contrast, the proposed method uses separate similarity measures
for each data representation, and allows for projection of previously unseen data
that are only observed in one representation (e.g. images but not text). We show
that this algorithm generalizes traditional spectral clustering algorithms and
show consistent empirical improvement over spectral clustering on a variety of
datasets of images with associated text.
article_processing_charge: No
author:
- first_name: Matthew
full_name: Blaschko, Matthew
last_name: Blaschko
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Blaschko M, Lampert C. Correlational spectral clustering. In: 2008 IEEE
Conference on Computer Vision and Pattern Recognition. IEEE; 2008:1-8. doi:10.1109/CVPR.2008.4587353'
apa: 'Blaschko, M., & Lampert, C. (2008). Correlational spectral clustering.
In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp.
1–8). Anchorage, AK, United States: IEEE. https://doi.org/10.1109/CVPR.2008.4587353'
chicago: Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.”
In 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8.
IEEE, 2008. https://doi.org/10.1109/CVPR.2008.4587353.
ieee: M. Blaschko and C. Lampert, “Correlational spectral clustering,” in 2008
IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK,
United States, 2008, pp. 1–8.
ista: 'Blaschko M, Lampert C. 2008. Correlational spectral clustering. 2008 IEEE
Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and
Pattern Recognition, 1–8.'
mla: Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.”
2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE,
2008, pp. 1–8, doi:10.1109/CVPR.2008.4587353.
short: M. Blaschko, C. Lampert, in:, 2008 IEEE Conference on Computer Vision and
Pattern Recognition, IEEE, 2008, pp. 1–8.
conference:
end_date: 2008-06-28
location: Anchorage, AK, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2008-06-23
date_created: 2018-12-11T12:04:45Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:32:08Z
day: '18'
doi: 10.1109/CVPR.2008.4587353
extern: '1'
language:
- iso: eng
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
isbn:
- '9781424422425'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2646'
status: public
title: Correlational spectral clustering
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...
---
OA_type: closed access
_id: '3714'
abstract:
- lang: eng
text: Most successful object recognition systems rely on binary classification,
deciding only if an object is present or not, but not providing information on
the actual object location. To perform localization, one can take a sliding window
approach, but this strongly increases the computational cost, because the classifier
function has to be evaluated over a large set of candidate subwindows. In this
paper, we propose a simple yet powerful branchand- bound scheme that allows efficient
maximization of a large class of classifier functions over all possible subimages.
It converges to a globally optimal solution typically in sublinear time. We show
how our method is applicable to different object detection and retrieval scenarios.
The achieved speedup allows the use of classifiers for localization that formerly
were considered too slow for this task, such as SVMs with a spatial pyramid kernel
or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art
performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC
2006 dataset and in the PASCAL VOC 2007 competition.
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthew
full_name: Blaschko, Matthew
last_name: Blaschko
- first_name: Thomas
full_name: Hofmann, Thomas
last_name: Hofmann
citation:
ama: 'Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization
by efficient subwindow search. In: 2008 IEEE Conference on Computer Vision
and Pattern Recognition. IEEE; 2008:1-8. doi:10.1109/CVPR.2008.4587586'
apa: 'Lampert, C., Blaschko, M., & Hofmann, T. (2008). Beyond sliding windows:
Object localization by efficient subwindow search. In 2008 IEEE Conference
on Computer Vision and Pattern Recognition (pp. 1–8). Anchorage, AK, United
States: IEEE. https://doi.org/10.1109/CVPR.2008.4587586'
chicago: 'Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding
Windows: Object Localization by Efficient Subwindow Search.” In 2008 IEEE Conference
on Computer Vision and Pattern Recognition, 1–8. IEEE, 2008. https://doi.org/10.1109/CVPR.2008.4587586.'
ieee: 'C. Lampert, M. Blaschko, and T. Hofmann, “Beyond sliding windows: Object
localization by efficient subwindow search,” in 2008 IEEE Conference on Computer
Vision and Pattern Recognition, Anchorage, AK, United States, 2008, pp. 1–8.'
ista: 'Lampert C, Blaschko M, Hofmann T. 2008. Beyond sliding windows: Object localization
by efficient subwindow search. 2008 IEEE Conference on Computer Vision and Pattern
Recognition. CVPR: Computer Vision and Pattern Recognition, 1–8.'
mla: 'Lampert, Christoph, et al. “Beyond Sliding Windows: Object Localization by
Efficient Subwindow Search.” 2008 IEEE Conference on Computer Vision and Pattern
Recognition, IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587586.'
short: C. Lampert, M. Blaschko, T. Hofmann, in:, 2008 IEEE Conference on Computer
Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.
conference:
end_date: 2008-06-28
location: Anchorage, AK, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2008-06-23
date_created: 2018-12-11T12:04:46Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2026-06-12T11:29:05Z
day: '18'
doi: 10.1109/CVPR.2008.4587586
extern: '1'
language:
- iso: eng
main_file_link:
- url: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf
month: '09'
oa_version: None
page: 1 - 8
publication: 2008 IEEE Conference on Computer Vision and Pattern Recognition
publication_identifier:
isbn:
- '9781424422425'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '2644'
status: public
title: 'Beyond sliding windows: Object localization by efficient subwindow search'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2008'
...
---
OA_type: closed access
_id: '3183'
abstract:
- lang: eng
text: This paper describes two algorithms capable of real-time segmentation of foreground
from background layers in stereo video sequences. Automatic separation of layers
from colour/contrast or from stereo alone is known to be error-prone. Here, colour,
contrast and stereo matching information are fused to infer layers accurately
and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves
stereo in an extended 6-state space that represents both foreground/background
layers and occluded regions. The stereo-match likelihood is then fused with a
contrast-sensitive colour model that is learned on the fly, and stereo disparities
are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC),
does not directly solve stereo. Instead the stereo match likelihood is marginalised
over foreground and background hypotheses, and fused with a contrast-sensitive
colour model like the one used in LDP. Segmentation is solved efficiently by ternary
graph cut. Both algorithms are evaluated with respect to ground truth data and
found to have similar perfomance, substantially better than stereo or colour/contrast
alone. However, their characteristics with respect to computational efficiency
are rather different. The algorithms are demonstrated in the application of background
substitution and shown to give good quality composite video output.
article_processing_charge: No
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Antonio
full_name: Criminisi, Antonio
last_name: Criminisi
- first_name: Andrew
full_name: Blake, Andrew
last_name: Blake
- first_name: Geoffrey
full_name: Cross, Geoffrey
last_name: Cross
- first_name: Carsten
full_name: Rother, Carsten
last_name: Rother
citation:
ama: 'Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. Bi-layer segmentation
of binocular stereo video. In: Proceedings of the 2005 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition Volume 2 . Vol 2. IEEE;
2005:407-414. doi:10.1109/CVPR.2005.91'
apa: 'Kolmogorov, V., Criminisi, A., Blake, A., Cross, G., & Rother, C. (2005).
Bi-layer segmentation of binocular stereo video. In Proceedings of the 2005
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Volume
2 (Vol. 2, pp. 407–414). San Diego, CA, United States: IEEE. https://doi.org/10.1109/CVPR.2005.91'
chicago: Kolmogorov, Vladimir, Antonio Criminisi, Andrew Blake, Geoffrey Cross,
and Carsten Rother. “Bi-Layer Segmentation of Binocular Stereo Video.” In Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume 2 , 2:407–14. IEEE, 2005. https://doi.org/10.1109/CVPR.2005.91.
ieee: V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, “Bi-layer
segmentation of binocular stereo video,” in Proceedings of the 2005 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition Volume 2 , San
Diego, CA, United States, 2005, vol. 2, pp. 407–414.
ista: 'Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. 2005. Bi-layer segmentation
of binocular stereo video. Proceedings of the 2005 IEEE Computer Society Conference
on Computer Vision and Pattern Recognition Volume 2 . CVPR: Computer Vision and
Pattern Recognition vol. 2, 407–414.'
mla: Kolmogorov, Vladimir, et al. “Bi-Layer Segmentation of Binocular Stereo Video.”
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision
and Pattern Recognition Volume 2 , vol. 2, IEEE, 2005, pp. 407–14, doi:10.1109/CVPR.2005.91.
short: V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, C. Rother, in:, Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume 2 , IEEE, 2005, pp. 407–414.
conference:
end_date: 2005-06-25
location: San Diego, CA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2005-06-20
das_tickbox: '1'
date_created: 2018-12-11T12:01:52Z
date_published: 2005-07-25T00:00:00Z
date_updated: 2026-07-02T09:00:36Z
day: '25'
doi: 10.1109/CVPR.2005.91
extern: '1'
intvolume: ' 2'
language:
- iso: eng
main_file_link:
- url: http://research.microsoft.com/pubs/67281/criminisi_cvpr2005.pdf
month: '07'
oa_version: None
page: 407 - 414
publication: 'Proceedings of the 2005 IEEE Computer Society Conference on Computer
Vision and Pattern Recognition Volume 2 '
publication_identifier:
isbn:
- '0769523722'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '3502'
status: public
title: Bi-layer segmentation of binocular stereo video
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2
year: '2005'
...
---
OA_type: closed access
_id: '3175'
abstract:
- lang: eng
text: This paper addresses the novel problem of automatically synthesizing an output
image from a large collection of different input images. The synthesized image,
called a digital tapestry, can be viewed as a visual summary or a virtual 'thumbnail'
of all the images in the input collection. The problem of creating the tapestry
is cast as a multi-class labeling problem such that each region in the tapestry
is constructed from input image blocks that are salient and such that neighboring
blocks satisfy spatial compatibility. This is formulated using a Markov Random
Field and optimized via the graph cut based expansion move algorithm. The standard
expansion move algorithm can only handle energies with metric terms, while our
energy contains non-metric (soft and hard) constraints. Therefore we propose two
novel contributions. First, we extend the expansion move algorithm for energy
functions with non-metric hard constraints. Secondly, we modify it for functions
with "almost" metric soft terms, and show that it gives good results
in practice. The proposed framework was tested on several consumer photograph
collections, and the results are presented.
article_processing_charge: No
author:
- first_name: Carsten
full_name: Rother, Carsten
last_name: Rother
- first_name: Sanjiv
full_name: Kumar, Sanjiv
last_name: Kumar
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Andrew
full_name: Blake, Andrew
last_name: Blake
citation:
ama: 'Rother C, Kumar S, Kolmogorov V, Blake A. Digital tapestry. In: Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume 1. Vol 1. IEEE; 2005:589-596. doi:10.1109/CVPR.2005.130'
apa: 'Rother, C., Kumar, S., Kolmogorov, V., & Blake, A. (2005). Digital tapestry.
In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision
and Pattern Recognition Volume 1 (Vol. 1, pp. 589–596). San Diego, CA, United
States: IEEE. https://doi.org/10.1109/CVPR.2005.130'
chicago: Rother, Carsten, Sanjiv Kumar, Vladimir Kolmogorov, and Andrew Blake. “Digital
Tapestry.” In Proceedings of the 2005 IEEE Computer Society Conference on Computer
Vision and Pattern Recognition Volume 1, 1:589–96. IEEE, 2005. https://doi.org/10.1109/CVPR.2005.130.
ieee: C. Rother, S. Kumar, V. Kolmogorov, and A. Blake, “Digital tapestry,” in Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume 1, San Diego, CA, United States, 2005, vol. 1, pp. 589–596.
ista: 'Rother C, Kumar S, Kolmogorov V, Blake A. 2005. Digital tapestry. Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume 1. CVPR: Computer Vision and Pattern Recognition vol. 1, 589–596.'
mla: Rother, Carsten, et al. “Digital Tapestry.” Proceedings of the 2005 IEEE
Computer Society Conference on Computer Vision and Pattern Recognition Volume
1, vol. 1, IEEE, 2005, pp. 589–96, doi:10.1109/CVPR.2005.130.
short: C. Rother, S. Kumar, V. Kolmogorov, A. Blake, in:, Proceedings of the 2005
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Volume
1, IEEE, 2005, pp. 589–596.
conference:
end_date: 2005-06-25
location: San Diego, CA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2005-06-20
das_tickbox: '1'
date_created: 2018-12-11T12:01:50Z
date_published: 2005-07-25T00:00:00Z
date_updated: 2026-07-02T09:06:43Z
day: '25'
doi: 10.1109/CVPR.2005.130
extern: '1'
intvolume: ' 1'
language:
- iso: eng
main_file_link:
- url: http://research.microsoft.com/en-us/um/people/ablake/papers/ablake/rother_cvpr05.pdf
month: '07'
oa_version: None
page: 589 - 596
publication: Proceedings of the 2005 IEEE Computer Society Conference on Computer
Vision and Pattern Recognition Volume 1
publication_identifier:
isbn:
- '0769523722'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '3503'
status: public
title: Digital tapestry
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 1
year: '2005'
...
---
OA_place: repository
OA_type: green
_id: '3176'
abstract:
- lang: eng
text: "This paper demonstrates the high quality, real-time segmentation techniques.
We achieve real-time segmentation of foreground from background layers in stereo
video sequences. Automatic separation of layers from colour/contrast or from stereo
alone is known to be error-prone. Here, colour, contrast and stereo matching information
are fused to infer layers accurately and efficiently. The first algorithm, layered
dynamic programming (LDP), solves stereo in an extended 6-state space that represents
both foreground/background layers and occluded regions. The stereo-match likelihood
is then fused with a contrast-sensitive colour model that is learned on the fly,
and stereo disparities are obtained by dynamic programming. The second algorithm,
layered graph cut (LGC), does not directly solve stereo. Instead the stereo match
likelihood is marginalised over foreground and background hypotheses, and fused
with a contrast-sensitive colour model like the one used in LDP. Segmentation
is solved efficiently by ternary graph cut. Both algorithms are evaluated with
respect to ground truth data and found to have similar performance, substantially
better than stereo or colour/contrast alone. However, their characteristics with
respect to computational efficiency are rather different. The algorithms are demonstrated
in the application of background substitution and shown to give good quality composite
video output.\r\n"
article_processing_charge: No
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Antonio
full_name: Criminisi, Antonio
last_name: Criminisi
- first_name: Andrew
full_name: Blake, Andrew
last_name: Blake
- first_name: Geoffrey
full_name: Cross, Geoffrey
last_name: Cross
- first_name: Carsten
full_name: Rother, Carsten
last_name: Rother
citation:
ama: 'Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. Bi-layer segmentation
of binocular stereo video. In: Proceedings of the 2005 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition. IEEE; 2005:1186-1186.
doi:10.1109/CVPR.2005.90'
apa: 'Kolmogorov, V., Criminisi, A., Blake, A., Cross, G., & Rother, C. (2005).
Bi-layer segmentation of binocular stereo video. In Proceedings of the 2005
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(pp. 1186–1186). San Diego, CA, United States: IEEE. https://doi.org/10.1109/CVPR.2005.90'
chicago: Kolmogorov, Vladimir, Antonio Criminisi, Andrew Blake, Geoffrey Cross,
and Carsten Rother. “Bi-Layer Segmentation of Binocular Stereo Video.” In Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
1186–1186. IEEE, 2005. https://doi.org/10.1109/CVPR.2005.90.
ieee: V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, “Bi-layer
segmentation of binocular stereo video,” in Proceedings of the 2005 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, San Diego,
CA, United States, 2005, pp. 1186–1186.
ista: 'Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. 2005. Bi-layer segmentation
of binocular stereo video. Proceedings of the 2005 IEEE Computer Society Conference
on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern
Recognition, 1186–1186.'
mla: Kolmogorov, Vladimir, et al. “Bi-Layer Segmentation of Binocular Stereo Video.”
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, IEEE, 2005, pp. 1186–1186, doi:10.1109/CVPR.2005.90.
short: V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, C. Rother, in:, Proceedings
of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
IEEE, 2005, pp. 1186–1186.
conference:
end_date: 2005-06-25
location: San Diego, CA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2005-06-20
das_tickbox: '1'
date_created: 2018-12-11T12:01:50Z
date_published: 2005-06-20T00:00:00Z
date_updated: 2026-07-15T12:24:09Z
day: '20'
doi: 10.1109/CVPR.2005.90
extern: '1'
language:
- iso: eng
month: '06'
oa_version: None
page: 1186 - 1186
publication: Proceedings of the 2005 IEEE Computer Society Conference on Computer
Vision and Pattern Recognition
publication_identifier:
isbn:
- '0769523722'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
publist_id: '3504'
status: public
title: Bi-layer segmentation of binocular stereo video
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2005'
...