---
_id: '3164'
abstract:
- lang: eng
text: Overview of the Special Issue on structured prediction and inference.
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. Guest editorial: Special issue on structured prediction
and inference. International Journal of Computer Vision. 2012;99(3):257-258.
doi:10.1007/s11263-012-0530-y'
apa: 'Blaschko, M., & Lampert, C. (2012). Guest editorial: Special issue on
structured prediction and inference. International Journal of Computer Vision.
Springer. https://doi.org/10.1007/s11263-012-0530-y'
chicago: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue
on Structured Prediction and Inference.” International Journal of Computer
Vision. Springer, 2012. https://doi.org/10.1007/s11263-012-0530-y.'
ieee: 'M. Blaschko and C. Lampert, “Guest editorial: Special issue on structured
prediction and inference,” International Journal of Computer Vision, vol.
99, no. 3. Springer, pp. 257–258, 2012.'
ista: 'Blaschko M, Lampert C. 2012. Guest editorial: Special issue on structured
prediction and inference. International Journal of Computer Vision. 99(3), 257–258.'
mla: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue
on Structured Prediction and Inference.” International Journal of Computer
Vision, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:10.1007/s11263-012-0530-y.'
short: M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012)
257–258.
date_created: 2018-12-11T12:01:46Z
date_published: 2012-09-01T00:00:00Z
date_updated: 2021-01-12T07:41:30Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/s11263-012-0530-y
intvolume: ' 99'
issue: '3'
language:
- iso: eng
month: '09'
oa_version: None
page: 257 - 258
publication: International Journal of Computer Vision
publication_status: published
publisher: Springer
publist_id: '3521'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Guest editorial: Special issue on structured prediction and inference'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 99
year: '2012'
...
---
_id: '3248'
abstract:
- lang: eng
text: We describe RTblob, a high speed vision system that detects objects in cluttered
scenes based on their color and shape at a speed of over 800 frames/s. Because
the system is available as open-source software and relies only on off-the-shelf
PC hardware components, it can provide the basis for multiple application scenarios.
As an illustrative example, we show how RTblob can be used in a robotic table
tennis scenario to estimate ball trajectories through 3D space simultaneously
from four cameras images at a speed of 200 Hz.
article_processing_charge: No
article_type: original
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: Lampert C, Peters J. Real-time detection of colored objects in multiple camera
streams with off-the-shelf hardware components. Journal of Real-Time Image
Processing. 2012;7(1):31-41. doi:10.1007/s11554-010-0168-3
apa: Lampert, C., & Peters, J. (2012). Real-time detection of colored objects
in multiple camera streams with off-the-shelf hardware components. Journal
of Real-Time Image Processing. Springer. https://doi.org/10.1007/s11554-010-0168-3
chicago: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects
in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal
of Real-Time Image Processing. Springer, 2012. https://doi.org/10.1007/s11554-010-0168-3.
ieee: C. Lampert and J. Peters, “Real-time detection of colored objects in multiple
camera streams with off-the-shelf hardware components,” Journal of Real-Time
Image Processing, vol. 7, no. 1. Springer, pp. 31–41, 2012.
ista: Lampert C, Peters J. 2012. Real-time detection of colored objects in multiple
camera streams with off-the-shelf hardware components. Journal of Real-Time Image
Processing. 7(1), 31–41.
mla: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects
in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal
of Real-Time Image Processing, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:10.1007/s11554-010-0168-3.
short: C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.
date_created: 2018-12-11T12:02:15Z
date_published: 2012-03-01T00:00:00Z
date_updated: 2022-05-24T08:05:40Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/s11554-010-0168-3
file:
- access_level: open_access
checksum: 241be47ea50e81a283bcf4c45b07e8cc
content_type: application/pdf
creator: kschuh
date_created: 2019-02-12T10:52:25Z
date_updated: 2020-07-14T12:46:04Z
file_id: '5958'
file_name: 2012_Springer_Lampert.pdf
file_size: 2933187
relation: main_file
file_date_updated: 2020-07-14T12:46:04Z
has_accepted_license: '1'
intvolume: ' 7'
issue: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Submitted Version
page: 31 - 41
publication: Journal of Real-Time Image Processing
publication_identifier:
eissn:
- 1861-8219
issn:
- 1861-8200
publication_status: published
publisher: Springer
publist_id: '3417'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Real-time detection of colored objects in multiple camera streams with off-the-shelf
hardware components
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7
year: '2012'
...
---
_id: '5396'
abstract:
- lang: eng
text: We consider the problem of inference in agraphical model with binary variables.
While in theory it is arguably preferable to compute marginal probabilities, in
practice researchers often use MAP inference due to the availability of efficient
discrete optimization algorithms. We bridge the gap between the two approaches
by introducing the Discrete Marginals technique in which approximate marginals
are obtained by minimizing an objective function with unary and pair-wise terms
over a discretized domain. This allows the use of techniques originally devel-oped
for MAP-MRF inference and learning. We explore two ways to set up the objective
function - by discretizing the Bethe free energy and by learning it from training
data. Experimental results show that for certain types of graphs a learned function
can out-perform the Bethe approximation. We also establish a link between the
Bethe free energy and submodular functions.
alternative_title:
- IST Austria Technical Report
author:
- first_name: Filip
full_name: Korc, Filip
id: 476A2FD6-F248-11E8-B48F-1D18A9856A87
last_name: Korc
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Korc F, Kolmogorov V, Lampert C. Approximating Marginals Using Discrete
Energy Minimization. IST Austria; 2012. doi:10.15479/AT:IST-2012-0003
apa: Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals
using discrete energy minimization. IST Austria. https://doi.org/10.15479/AT:IST-2012-0003
chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. Approximating
Marginals Using Discrete Energy Minimization. IST Austria, 2012. https://doi.org/10.15479/AT:IST-2012-0003.
ieee: F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete
energy minimization. IST Austria, 2012.
ista: Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete
energy minimization, IST Austria, 13p.
mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization.
IST Austria, 2012, doi:10.15479/AT:IST-2012-0003.
short: F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete
Energy Minimization, IST Austria, 2012.
date_created: 2018-12-12T11:39:06Z
date_published: 2012-07-23T00:00:00Z
date_updated: 2023-02-23T11:13:22Z
day: '23'
ddc:
- '000'
department:
- _id: VlKo
- _id: ChLa
doi: 10.15479/AT:IST-2012-0003
file:
- access_level: open_access
checksum: 7e0ba85ad123b13223aaf6cdde2d288c
content_type: application/pdf
creator: system
date_created: 2018-12-12T11:53:29Z
date_updated: 2020-07-14T12:46:44Z
file_id: '5490'
file_name: IST-2012-0003_IST-2012-0003.pdf
file_size: 618744
relation: main_file
file_date_updated: 2020-07-14T12:46:44Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '13'
publication_identifier:
issn:
- 2664-1690
publication_status: published
publisher: IST Austria
pubrep_id: '36'
related_material:
record:
- id: '3124'
relation: earlier_version
status: public
status: public
title: Approximating marginals using discrete energy minimization
type: technical_report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '3163'
abstract:
- lang: eng
text: We study multi-label prediction for structured output sets, a problem that
occurs, for example, in object detection in images, secondary structure prediction
in computational biology, and graph matching with symmetries. Conventional multilabel
classification techniques are typically not applicable in this situation, because
they require explicit enumeration of the label set, which is infeasible in case
of structured outputs. Relying on techniques originally designed for single-label
structured prediction, in particular structured support vector machines, results
in reduced prediction accuracy, or leads to infeasible optimization problems.
In this work we derive a maximum-margin training formulation for multi-label structured
prediction that remains computationally tractable while achieving high prediction
accuracy. It also shares most beneficial properties with single-label maximum-margin
approaches, in particular formulation as a convex optimization problem, efficient
working set training, and PAC-Bayesian generalization bounds.
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. Maximum margin multi-label structured prediction. In: Neural Information
Processing Systems; 2011.'
apa: 'Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented
at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information
Processing Systems.'
chicago: Lampert, Christoph. “Maximum Margin Multi-Label Structured Prediction.”
Neural Information Processing Systems, 2011.
ieee: 'C. Lampert, “Maximum margin multi-label structured prediction,” presented
at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.'
ista: 'Lampert C. 2011. Maximum margin multi-label structured prediction. NIPS:
Neural Information Processing Systems.'
mla: Lampert, Christoph. Maximum Margin Multi-Label Structured Prediction.
Neural Information Processing Systems, 2011.
short: C. Lampert, in:, Neural Information Processing Systems, 2011.
conference:
end_date: 2011-12-14
location: Granada, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2011-12-12
date_created: 2018-12-11T12:01:45Z
date_published: 2011-12-01T00:00:00Z
date_updated: 2023-10-17T11:47:35Z
day: '01'
department:
- _id: ChLa
language:
- iso: eng
month: '12'
oa_version: None
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '3522'
quality_controlled: '1'
related_material:
record:
- id: '3322'
relation: later_version
status: public
scopus_import: 1
status: public
title: Maximum margin multi-label structured prediction
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3319'
abstract:
- lang: eng
text: We address the problem of metric learning for multi-view data, namely the
construction of embedding projections from data in different representations into
a shared feature space, such that the Euclidean distance in this space provides
a meaningful within-view as well as between-view similarity. Our motivation stems
from the problem of cross-media retrieval tasks, where the availability of a joint
Euclidean distance function is a pre-requisite to allow fast, in particular hashing-based,
nearest neighbor queries. We formulate an objective function that expresses the
intuitive concept that matching samples are mapped closely together in the output
space, whereas non-matching samples are pushed apart, no matter in which view
they are available. The resulting optimization problem is not convex, but it can
be decomposed explicitly into a convex and a concave part, thereby allowing efficient
optimization using the convex-concave procedure. Experiments on an image retrieval
task show that nearest-neighbor based cross-view retrieval is indeed possible,
and the proposed technique improves the retrieval accuracy over baseline techniques.
article_processing_charge: No
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Quadrianto N, Lampert C. Learning multi-view neighborhood preserving projections.
In: ML Research Press; 2011:425-432.'
apa: 'Quadrianto, N., & Lampert, C. (2011). Learning multi-view neighborhood
preserving projections (pp. 425–432). Presented at the ICML: International Conference
on Machine Learning, Bellevue, United States: ML Research Press.'
chicago: Quadrianto, Novi, and Christoph Lampert. “Learning Multi-View Neighborhood
Preserving Projections,” 425–32. ML Research Press, 2011.
ieee: 'N. Quadrianto and C. Lampert, “Learning multi-view neighborhood preserving
projections,” presented at the ICML: International Conference on Machine Learning,
Bellevue, United States, 2011, pp. 425–432.'
ista: 'Quadrianto N, Lampert C. 2011. Learning multi-view neighborhood preserving
projections. ICML: International Conference on Machine Learning, 425–432.'
mla: Quadrianto, Novi, and Christoph Lampert. Learning Multi-View Neighborhood
Preserving Projections. ML Research Press, 2011, pp. 425–32.
short: N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432.
conference:
end_date: 2011-07-02
location: Bellevue, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2011-06-28
date_created: 2018-12-11T12:02:39Z
date_published: 2011-01-01T00:00:00Z
date_updated: 2023-10-17T11:59:50Z
day: '01'
department:
- _id: ChLa
language:
- iso: eng
month: '01'
oa_version: None
page: 425 - 432
publication_status: published
publisher: ML Research Press
publist_id: '3316'
scopus_import: '1'
status: public
title: Learning multi-view neighborhood preserving projections
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3320'
abstract:
- lang: eng
text: Powerful statistical models that can be learned efficiently from large amounts
of data are currently revolutionizing computer vision. These models possess a
rich internal structure reflecting task-specific relations and constraints. This
monograph introduces the reader to the most popular classes of structured models
in computer vision. Our focus is discrete undirected graphical models which we
cover in detail together with a description of algorithms for both probabilistic
inference and maximum a posteriori inference. We discuss separately recently successful
techniques for prediction in general structured models. In the second part of
this monograph we describe methods for parameter learning where we distinguish
the classic maximum likelihood based methods from the more recent prediction-based
parameter learning methods. We highlight developments to enhance current models
and discuss kernelized models and latent variable models. To make the monograph
more practical and to provide links to further study we provide examples of successful
application of many methods in the computer vision literature.
article_processing_charge: No
article_type: original
author:
- first_name: Sebastian
full_name: Nowozin, Sebastian
last_name: Nowozin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Nowozin S, Lampert C. Structured learning and prediction in computer vision.
Foundations and Trends in Computer Graphics and Vision. 2011;6(3-4):185-365.
doi:10.1561/0600000033
apa: Nowozin, S., & Lampert, C. (2011). Structured learning and prediction in
computer vision. Foundations and Trends in Computer Graphics and Vision.
Now Publishers. https://doi.org/10.1561/0600000033
chicago: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction
in Computer Vision.” Foundations and Trends in Computer Graphics and Vision.
Now Publishers, 2011. https://doi.org/10.1561/0600000033.
ieee: S. Nowozin and C. Lampert, “Structured learning and prediction in computer
vision,” Foundations and Trends in Computer Graphics and Vision, vol. 6,
no. 3–4. Now Publishers, pp. 185–365, 2011.
ista: Nowozin S, Lampert C. 2011. Structured learning and prediction in computer
vision. Foundations and Trends in Computer Graphics and Vision. 6(3–4), 185–365.
mla: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction
in Computer Vision.” Foundations and Trends in Computer Graphics and Vision,
vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:10.1561/0600000033.
short: S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision
6 (2011) 185–365.
date_created: 2018-12-11T12:02:39Z
date_published: 2011-05-23T00:00:00Z
date_updated: 2023-10-17T11:52:46Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1561/0600000033
file:
- access_level: open_access
checksum: f1043ef389f1558e2a226bb51568511f
content_type: application/pdf
creator: dernst
date_created: 2020-05-14T14:34:47Z
date_updated: 2020-07-14T12:46:07Z
file_id: '7837'
file_name: 2011_CompGraphicsVision_Nowozin.pdf
file_size: 3745064
relation: main_file
file_date_updated: 2020-07-14T12:46:07Z
has_accepted_license: '1'
intvolume: ' 6'
issue: 3-4
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 185 - 365
publication: Foundations and Trends in Computer Graphics and Vision
publication_status: published
publisher: Now Publishers
publist_id: '3315'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Structured learning and prediction in computer vision
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2011'
...
---
_id: '3322'
abstract:
- lang: eng
text: We study multi-label prediction for structured output spaces, a problem that
occurs, for example, in object detection in images, secondary structure prediction
in computational biology, and graph matching with symmetries. Conventional multi-label
classification techniques are typically not applicable in this situation, because
they require explicit enumeration of the label space, which is infeasible in case
of structured outputs. Relying on techniques originally designed for single- label
structured prediction, in particular structured support vector machines, results
in reduced prediction accuracy, or leads to infeasible optimization problems.
In this work we derive a maximum-margin training formulation for multi-label structured
prediction that remains computationally tractable while achieving high prediction
accuracy. It also shares most beneficial properties with single-label maximum-margin
approaches, in particular a formulation as a convex optimization problem, efficient
working set training, and PAC-Bayesian generalization bounds.
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. Maximum Margin Multi Label Structured Prediction. Neural
Information Processing Systems Foundation; 2011.
apa: 'Lampert, C. (2011). Maximum margin multi label structured prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation.'
chicago: 'Lampert, Christoph. Maximum Margin Multi Label Structured Prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation, 2011.'
ieee: C. Lampert, Maximum margin multi label structured prediction. Neural
Information Processing Systems Foundation, 2011.
ista: Lampert C. 2011. Maximum margin multi label structured prediction, Neural
Information Processing Systems Foundation,p.
mla: 'Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” NIPS:
Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2011.'
short: C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information
Processing Systems Foundation, 2011.
date_created: 2018-12-11T12:02:40Z
date_published: 2011-12-13T00:00:00Z
date_updated: 2023-10-17T11:47:36Z
day: '13'
department:
- _id: ChLa
language:
- iso: eng
month: '12'
oa_version: None
publication: 'NIPS: Neural Information Processing Systems'
publication_status: published
publisher: Neural Information Processing Systems Foundation
publist_id: '3313'
related_material:
record:
- id: '3163'
relation: earlier_version
status: public
status: public
title: Maximum margin multi label structured prediction
type: conference_poster
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3336'
abstract:
- lang: eng
text: 'We introduce TopoCut: a new way to integrate knowledge about topological
properties (TPs) into random field image segmentation model. Instead of including
TPs as additional constraints during minimization of the energy function, we devise
an efficient algorithm for modifying the unary potentials such that the resulting
segmentation is guaranteed with the desired properties. Our method is more flexible
in the sense that it handles more topology constraints than previous methods,
which were only able to enforce pairwise or global connectivity. In particular,
our method is very fast, making it for the first time possible to enforce global
topological properties in practical image segmentation tasks.'
acknowledgement: The first author is supported by the Austrian Science Fund (FWF)
grant No. P20134-N13. The authors would like to thank Sebastian Nowozin for helpful
discussions.
article_processing_charge: No
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Daniel
full_name: Freedman, Daniel
last_name: Freedman
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Chen C, Freedman D, Lampert C. Enforcing topological constraints in random
field image segmentation. In: CVPR: Computer Vision and Pattern Recognition.
IEEE; 2011:2089-2096. doi:10.1109/CVPR.2011.5995503'
apa: 'Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints
in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition
(pp. 2089–2096). Colorado Springs, CO, United States: IEEE. https://doi.org/10.1109/CVPR.2011.5995503'
chicago: 'Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological
Constraints in Random Field Image Segmentation.” In CVPR: Computer Vision and
Pattern Recognition, 2089–96. IEEE, 2011. https://doi.org/10.1109/CVPR.2011.5995503.'
ieee: 'C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints
in random field image segmentation,” in CVPR: Computer Vision and Pattern Recognition,
Colorado Springs, CO, United States, 2011, pp. 2089–2096.'
ista: 'Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in
random field image segmentation. CVPR: Computer Vision and Pattern Recognition.
CVPR: Conference on Computer Vision and Pattern Recognition, 2089–2096.'
mla: 'Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image
Segmentation.” CVPR: Computer Vision and Pattern Recognition, IEEE, 2011,
pp. 2089–96, doi:10.1109/CVPR.2011.5995503.'
short: 'C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern
Recognition, IEEE, 2011, pp. 2089–2096.'
conference:
end_date: 2011-06-25
location: Colorado Springs, CO, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2011-06-20
date_created: 2018-12-11T12:02:45Z
date_published: 2011-07-22T00:00:00Z
date_updated: 2023-02-23T12:23:56Z
day: '22'
department:
- _id: HeEd
- _id: ChLa
doi: 10.1109/CVPR.2011.5995503
language:
- iso: eng
month: '07'
oa_version: None
page: 2089 - 2096
publication: 'CVPR: Computer Vision and Pattern Recognition'
publication_identifier:
eisbn:
- 978-1-4577-0395-9
isbn:
- 978-1-4577-0394-2
publication_status: published
publisher: IEEE
publist_id: '3294'
quality_controlled: '1'
related_material:
record:
- id: '5386'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Enforcing topological constraints in random field image segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3337'
abstract:
- lang: eng
text: Playing table tennis is a difficult task for robots, especially due to their
limitations of acceleration. A key bottleneck is the amount of time needed to
reach the desired hitting position and velocity of the racket for returning the
incoming ball. Here, it often does not suffice to simply extrapolate the ball's
trajectory after the opponent returns it but more information is needed. Humans
are able to predict the ball's trajectory based on the opponent's moves and, thus,
have a considerable advantage. Hence, we propose to incorporate an anticipation
system into robot table tennis players, which enables the robot to react earlier
while the opponent is performing the striking movement. Based on visual observation
of the opponent's racket movement, the robot can predict the aim of the opponent
and adjust its movement generation accordingly. The policies for deciding how
and when to react are obtained by reinforcement learning. We conduct experiments
with an existing robot player to show that the learned reaction policy can significantly
improve the performance of the overall system.
author:
- first_name: Zhikun
full_name: Wang, Zhikun
last_name: Wang
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Katharina
full_name: Mülling, Katharina
last_name: Mülling
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation
policies for robot table tennis. In: IEEE; 2011:332-337. doi:10.1109/IROS.2011.6094892'
apa: 'Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., & Peters, J. (2011).
Learning anticipation policies for robot table tennis (pp. 332–337). Presented
at the IROS: RSJ International Conference on Intelligent Robots and Systems, San
Francisco, USA: IEEE. https://doi.org/10.1109/IROS.2011.6094892'
chicago: Wang, Zhikun, Christoph Lampert, Katharina Mülling, Bernhard Schölkopf,
and Jan Peters. “Learning Anticipation Policies for Robot Table Tennis,” 332–37.
IEEE, 2011. https://doi.org/10.1109/IROS.2011.6094892.
ieee: 'Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation
policies for robot table tennis,” presented at the IROS: RSJ International Conference
on Intelligent Robots and Systems, San Francisco, USA, 2011, pp. 332–337.'
ista: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. 2011. Learning anticipation
policies for robot table tennis. IROS: RSJ International Conference on Intelligent
Robots and Systems, 332–337.'
mla: Wang, Zhikun, et al. Learning Anticipation Policies for Robot Table Tennis.
IEEE, 2011, pp. 332–37, doi:10.1109/IROS.2011.6094892.
short: Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011,
pp. 332–337.
conference:
end_date: 2011-09-30
location: San Francisco, USA
name: 'IROS: RSJ International Conference on Intelligent Robots and Systems'
start_date: 2011-09-25
date_created: 2018-12-11T12:02:45Z
date_published: 2011-01-01T00:00:00Z
date_updated: 2021-01-12T07:42:45Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/IROS.2011.6094892
language:
- iso: eng
month: '01'
oa_version: None
page: 332 - 337
publication_status: published
publisher: IEEE
publist_id: '3293'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning anticipation policies for robot table tennis
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '5386'
abstract:
- lang: eng
text: 'We introduce TopoCut: a new way to integrate knowledge about topological
properties (TPs) into random field image segmentation model. Instead of including
TPs as additional constraints during minimization of the energy function, we devise
an efficient algorithm for modifying the unary potentials such that the resulting
segmentation is guaranteed with the desired properties. Our method is more flexible
in the sense that it handles more topology constraints than previous methods,
which were only able to enforce pairwise or global connectivity. In particular,
our method is very fast, making it for the first time possible to enforce global
topological properties in practical image segmentation tasks.'
alternative_title:
- IST Austria Technical Report
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Daniel
full_name: Freedman, Daniel
last_name: Freedman
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Chen C, Freedman D, Lampert C. Enforcing Topological Constraints in Random
Field Image Segmentation. IST Austria; 2011. doi:10.15479/AT:IST-2011-0002
apa: Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological
constraints in random field image segmentation. IST Austria. https://doi.org/10.15479/AT:IST-2011-0002
chicago: Chen, Chao, Daniel Freedman, and Christoph Lampert. Enforcing Topological
Constraints in Random Field Image Segmentation. IST Austria, 2011. https://doi.org/10.15479/AT:IST-2011-0002.
ieee: C. Chen, D. Freedman, and C. Lampert, Enforcing topological constraints
in random field image segmentation. IST Austria, 2011.
ista: Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in
random field image segmentation, IST Austria, 69p.
mla: Chen, Chao, et al. Enforcing Topological Constraints in Random Field Image
Segmentation. IST Austria, 2011, doi:10.15479/AT:IST-2011-0002.
short: C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random
Field Image Segmentation, IST Austria, 2011.
date_created: 2018-12-12T11:39:02Z
date_published: 2011-03-28T00:00:00Z
date_updated: 2023-02-23T11:22:48Z
day: '28'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.15479/AT:IST-2011-0002
file:
- access_level: open_access
checksum: ad64c2add5fe2ad10e9d5c669f3f9526
content_type: application/pdf
creator: system
date_created: 2018-12-12T11:53:34Z
date_updated: 2020-07-14T12:46:41Z
file_id: '5495'
file_name: IST-2011-0002_IST-2011-0002.pdf
file_size: 26390601
relation: main_file
file_date_updated: 2020-07-14T12:46:41Z
has_accepted_license: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '69'
publication_identifier:
issn:
- 2664-1690
publication_status: published
publisher: IST Austria
pubrep_id: '22'
related_material:
record:
- id: '3336'
relation: later_version
status: public
status: public
title: Enforcing topological constraints in random field image segmentation
type: technical_report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3382'
abstract:
- lang: eng
text: Dynamic tactile sensing is a fundamental ability to recognize materials and
objects. However, while humans are born with partially developed dynamic tactile
sensing and quickly master this skill, today's robots remain in their infancy.
The development of such a sense requires not only better sensors but the right
algorithms to deal with these sensors' data as well. For example, when classifying
a material based on touch, the data are noisy, high-dimensional, and contain irrelevant
signals as well as essential ones. Few classification methods from machine learning
can deal with such problems. In this paper, we propose an efficient approach to
infer suitable lower dimensional representations of the tactile data. In order
to classify materials based on only the sense of touch, these representations
are autonomously discovered using visual information of the surfaces during training.
However, accurately pairing vision and tactile samples in real-robot applications
is a difficult problem. The proposed approach, therefore, works with weak pairings
between the modalities. Experiments show that the resulting approach is very robust
and yields significantly higher classification performance based on only dynamic
tactile sensing.
author:
- first_name: Oliver
full_name: Kroemer, Oliver
last_name: Kroemer
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust
vision based training. IEEE Transactions on Robotics. 2011;27(3):545-557.
doi:10.1109/TRO.2011.2121130
apa: Kroemer, O., Lampert, C., & Peters, J. (2011). Learning dynamic tactile
sensing with robust vision based training. IEEE Transactions on Robotics.
IEEE. https://doi.org/10.1109/TRO.2011.2121130
chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile
Sensing with Robust Vision Based Training.” IEEE Transactions on Robotics.
IEEE, 2011. https://doi.org/10.1109/TRO.2011.2121130.
ieee: O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with
robust vision based training,” IEEE Transactions on Robotics, vol. 27,
no. 3. IEEE, pp. 545–557, 2011.
ista: Kroemer O, Lampert C, Peters J. 2011. Learning dynamic tactile sensing with
robust vision based training. IEEE Transactions on Robotics. 27(3), 545–557.
mla: Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision
Based Training.” IEEE Transactions on Robotics, vol. 27, no. 3, IEEE, 2011,
pp. 545–57, doi:10.1109/TRO.2011.2121130.
short: O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011)
545–557.
date_created: 2018-12-11T12:03:01Z
date_published: 2011-05-21T00:00:00Z
date_updated: 2021-01-12T07:43:06Z
day: '21'
department:
- _id: ChLa
doi: 10.1109/TRO.2011.2121130
intvolume: ' 27'
issue: '3'
language:
- iso: eng
month: '05'
oa_version: None
page: 545 - 557
publication: IEEE Transactions on Robotics
publication_status: published
publisher: IEEE
publist_id: '3225'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning dynamic tactile sensing with robust vision based training
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2011'
...
---
_id: '3389'
abstract:
- lang: eng
text: Kernel canonical correlation analysis (KCCA) is a general technique for subspace
learning that incorporates principal components analysis (PCA) and Fisher linear
discriminant analysis (LDA) as special cases. By finding directions that maximize
correlation, KCCA learns representations that are more closely tied to the underlying
process that generates the data and can ignore high-variance noise directions.
However, for data where acquisition in one or more modalities is expensive or
otherwise limited, KCCA may suffer from small sample effects. We propose to use
semi-supervised Laplacian regularization to utilize data that are present in only
one modality. This approach is able to find highly correlated directions that
also lie along the data manifold, resulting in a more robust estimate of correlated
subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally
amenable to subspace techniques as data are well aligned. fMRI data of the human
brain are a particularly interesting candidate. In this study we implemented various
supervised and semi-supervised versions of KCCA on human fMRI data, with regression
to single and multi-variate labels (corresponding to video content subjects viewed
during the image acquisition). In each variate condition, the semi-supervised
variants of KCCA performed better than the supervised variants, including a supervised
variant with Laplacian regularization. We additionally analyze the weights learned
by the regression in order to infer brain regions that are important to different
types of visual processing.
acknowledgement: The research leading to these results has received funding from the
European Research Council under the European Community’s Seventh Framework Programme
(FP7/2007-2013)/ERC Grant Agreement No. 228180. This work was funded in part by
the EC project CLASS, IST 027978, and the PASCAL2 network of excellence, IST 2002-506778.
author:
- first_name: Matthew
full_name: Blaschko, Matthew
last_name: Blaschko
- first_name: Jacquelyn
full_name: Shelton, Jacquelyn
last_name: Shelton
- first_name: Andreas
full_name: Bartels, Andreas
last_name: Bartels
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Arthur
full_name: Gretton, Arthur
last_name: Gretton
citation:
ama: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel
canonical correlation analysis with application to human fMRI. Pattern Recognition
Letters. 2011;32(11):1572-1583. doi:10.1016/j.patrec.2011.02.011
apa: Blaschko, M., Shelton, J., Bartels, A., Lampert, C., & Gretton, A. (2011).
Semi supervised kernel canonical correlation analysis with application to human
fMRI. Pattern Recognition Letters. Elsevier. https://doi.org/10.1016/j.patrec.2011.02.011
chicago: Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert,
and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with
Application to Human FMRI.” Pattern Recognition Letters. Elsevier, 2011.
https://doi.org/10.1016/j.patrec.2011.02.011.
ieee: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised
kernel canonical correlation analysis with application to human fMRI,” Pattern
Recognition Letters, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011.
ista: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. 2011. Semi supervised
kernel canonical correlation analysis with application to human fMRI. Pattern
Recognition Letters. 32(11), 1572–1583.
mla: Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis
with Application to Human FMRI.” Pattern Recognition Letters, vol. 32,
no. 11, Elsevier, 2011, pp. 1572–83, doi:10.1016/j.patrec.2011.02.011.
short: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition
Letters 32 (2011) 1572–1583.
date_created: 2018-12-11T12:03:03Z
date_published: 2011-08-01T00:00:00Z
date_updated: 2021-01-12T07:43:09Z
day: '01'
department:
- _id: ChLa
doi: 10.1016/j.patrec.2011.02.011
intvolume: ' 32'
issue: '11'
language:
- iso: eng
month: '08'
oa_version: None
page: 1572 - 1583
publication: Pattern Recognition Letters
publication_status: published
publisher: Elsevier
publist_id: '3218'
quality_controlled: '1'
scopus_import: 1
status: public
title: Semi supervised kernel canonical correlation analysis with application to human
fMRI
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2011'
...
---
_id: '3793'
abstract:
- lang: eng
text: "Recent progress in per-pixel object class labeling of natural images can
be attributed to the use of multiple types of image features and sound statistical
learning approaches. Within the latter, Conditional Random Fields (CRF) are prominently
used for their ability to represent interactions between random variables. Despite
their popularity in computer vision, parameter learning for CRFs has remained
difficult, popular approaches being cross-validation and piecewise training.\r\nIn
this work, we propose a simple yet expressive tree-structured CRF based on a recent
hierarchical image segmentation method. Our model combines and weights multiple
image features within a hierarchical representation and allows simple and efficient
globally-optimal learning of ≈ 105 parameters. The tractability of our model allows
us to pose and answer some of the open questions regarding parameter learning
applying to CRF-based approaches. The key findings for learning CRF models are,
from the obvious to the surprising, i) multiple image features always help, ii)
the limiting dimension with respect to current models is the amount of training
data, iii) piecewise training is competitive, iv) current methods for max-margin
training fail for models with many parameters.\r\n"
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Sebastian
full_name: Nowozin, Sebastian
last_name: Nowozin
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches
to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:10.1007/978-3-642-15567-3_8'
apa: 'Nowozin, S., Gehler, P., & Lampert, C. (2010). On parameter learning in
CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111).
Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete,
Greece: Springer. https://doi.org/10.1007/978-3-642-15567-3_8'
chicago: Nowozin, Sebastian, Peter Gehler, and Christoph Lampert. “On Parameter
Learning in CRF-Based Approaches to Object Class Image Segmentation,” 6316:98–111.
Springer, 2010. https://doi.org/10.1007/978-3-642-15567-3_8.
ieee: 'S. Nowozin, P. Gehler, and C. Lampert, “On parameter learning in CRF-based
approaches to object class image segmentation,” presented at the ECCV: European
Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6316, pp.
98–111.'
ista: 'Nowozin S, Gehler P, Lampert C. 2010. On parameter learning in CRF-based
approaches to object class image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 6316, 98–111.'
mla: Nowozin, Sebastian, et al. On Parameter Learning in CRF-Based Approaches
to Object Class Image Segmentation. Vol. 6316, Springer, 2010, pp. 98–111,
doi:10.1007/978-3-642-15567-3_8.
short: S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.
conference:
end_date: 2010-09-11
location: Heraklion, Crete, Greece
name: 'ECCV: European Conference on Computer Vision'
start_date: 2010-09-05
date_created: 2018-12-11T12:05:12Z
date_published: 2010-11-04T00:00:00Z
date_updated: 2021-01-12T07:52:14Z
day: '04'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-15567-3_8
file:
- access_level: open_access
checksum: 3716e10e161f7c714fd17ec193a223c3
content_type: application/pdf
creator: dernst
date_created: 2020-05-19T16:27:34Z
date_updated: 2020-07-14T12:46:16Z
file_id: '7871'
file_name: 2010_ECCV_Nowozin.pdf
file_size: 4087332
relation: main_file
file_date_updated: 2020-07-14T12:46:16Z
has_accepted_license: '1'
intvolume: ' 6316'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Submitted Version
page: 98 - 111
publication_status: published
publisher: Springer
publist_id: '2431'
quality_controlled: '1'
scopus_import: 1
status: public
title: On parameter learning in CRF-based approaches to object class image segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6316
year: '2010'
...
---
_id: '3794'
abstract:
- lang: eng
text: 'We study the problem of multimodal dimensionality reduction assuming that
data samples can be missing at training time, and not all data modalities may
be present at application time. Maximum covariance analysis, as a generalization
of PCA, has many desirable properties, but its application to practical problems
is limited by its need for perfectly paired data. We overcome this limitation
by a latent variable approach that allows working with weakly paired data and
is still able to efficiently process large datasets using standard numerical routines.
The resulting weakly paired maximum covariance analysis often finds better representations
than alternative methods, as we show in two exemplary tasks: texture discrimination
and transfer learning.'
alternative_title:
- LNCS
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Oliver
full_name: Krömer, Oliver
last_name: Krömer
citation:
ama: 'Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal
dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579.
doi:10.1007/978-3-642-15552-9_41'
apa: 'Lampert, C., & Krömer, O. (2010). Weakly-paired maximum covariance analysis
for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp.
566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion,
Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15552-9_41'
chicago: Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance
Analysis for Multimodal Dimensionality Reduction and Transfer Learning,” 6312:566–79.
Springer, 2010. https://doi.org/10.1007/978-3-642-15552-9_41.
ieee: 'C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for
multimodal dimensionality reduction and transfer learning,” presented at the ECCV:
European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312,
pp. 566–579.'
ista: 'Lampert C, Krömer O. 2010. Weakly-paired maximum covariance analysis for
multimodal dimensionality reduction and transfer learning. ECCV: European Conference
on Computer Vision, LNCS, vol. 6312, 566–579.'
mla: Lampert, Christoph, and Oliver Krömer. Weakly-Paired Maximum Covariance
Analysis for Multimodal Dimensionality Reduction and Transfer Learning. Vol.
6312, Springer, 2010, pp. 566–79, doi:10.1007/978-3-642-15552-9_41.
short: C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.
conference:
end_date: 2010-09-11
location: Heraklion, Crete, Greece
name: 'ECCV: European Conference on Computer Vision'
start_date: 2010-09-05
date_created: 2018-12-11T12:05:12Z
date_published: 2010-11-10T00:00:00Z
date_updated: 2021-01-12T07:52:14Z
day: '10'
department:
- _id: ChLa
doi: 10.1007/978-3-642-15552-9_41
intvolume: ' 6312'
language:
- iso: eng
main_file_link:
- url: http://www.ics.forth.gr/eccv2010/intro.php
month: '11'
oa_version: None
page: 566 - 579
publication_status: published
publisher: Springer
publist_id: '2433'
quality_controlled: '1'
scopus_import: 1
status: public
title: Weakly-paired maximum covariance analysis for multimodal dimensionality reduction
and transfer learning
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 6312
year: '2010'
...