MMV Research Laboratory: A
Retrospective Around Multimedia and
Computer Vision Projects Ivan Cabezas
July 18th 2012
Universidad Señor de Sipán – Chiclayo, Peru
Slide 2
Content
Universidad del Valle A Brief in Figures
Multimedia and Vision Laboratory National Cooperation
Industrial Collaboration
International Cooperation
Research Interests
A Camera Model
Some Research Projects
MPEG7 - SOS
Char Morphology
An Evaluation Methodology for Stereo Correspondence Algorithms
Final Remarks
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 3
The Universidad del Valle
The Universidad del Valle is the largest university in the south west of
Colombia
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
http://www.univalle.edu.co
Slide 4
Universidad del Valle: A Brief in Figures
Its main campus, Meléndez, has an extension of a million of square meters
There are two campus in Cali, and nine regionals in Valle and Cauca
There are 187 study programs offered in Cali, most of them for graduate
There are six faculties and two institutes
At February of 2012, it had a population of 27094 students
(88.7% undergraduate)
At December of 2011, it had 889 full time professors
(92% graduate, 30% PhD)
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
http://www.univalle.edu.co
Slide 5
Multimedia and Vision Laboratory
MMV is a multidisciplinary research group of the EISC
Ivan at WAC
2012
LACNEM, 2009
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Meetings, 2007 & 2011
Maria at UNAL
2011
INTERACTIVIA, 2009
http://www.lacnem.org
Slide 6
National Cooperation
John W. Branch, UNAL- Medellín
Cesar Collazos, UniCauca - Popayán
Fabio González, UNAL - Bogotá
Liliana Salazar
Escuela de Ciencias Básicas
Doris Hinestroza
Departamento de Matemáticas
Juan Barraza
Escuela de Ingeniería Química
Janet Sanabria, Escuela de Recursos Naturales y
del Ambiente
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 7
Industrial Collaborations
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 8
International Cooperation
Ebroul Izquierdo Head of the Multimedia
and Vision Research Group, School of
Electronic Engineering and Computer
Science, Queen Mary University of London
Aggelos Katsaggelos, Director Motorola
Center for Seamless Communications,
Northwestern University, USA
Panos Liatsis, Head of the Information
Engineering and Medical Imaging Group,
School of Engineering and Mathematical
Sciences, City University London
Sergio Velastin, Director Digital Imaging
Research Centre, Kingston University, UK
Valia Guerra, Instituto de Cibernética,
Matemática y Física (ICIMAF), Cuba
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 9
Research Interests
Multimedia and Computer Vision
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Computing System
Images
Information
Computer Vision http://www.slideshare.net/mmv-lab-univalle http://vision.mas.ecp.fr/Personnel/teboul/index.php/
Slide 10
A Camera Model
A Camera is a sensor following a model
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
http://quarknet.fnal.gov/fnal-uc/quarknet-summer-research/QNET2010/Astronomy/ http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html
Camera System
3D World
2D Images
http://www.univalle.edu.co
Slide 11
Some Research Projects
MPEG-7 UV
MPEG-7 SOS
Prokaryota
Vitisoft
Clusters: Espacial +
K - Means
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 12
MPEG-7 SOS: Motivation
M. Florian and M. Trujillo, Relational Database Schema for MPEG-7 Visual Descriptors, IEEE CBIR, 2008
M. Florian and M. Trujillo, Resource Oriented Architecture for Managing Multimedia Content, LACNEM, 2009
M. Florian MPEG-7 Service Oriented System, Master Research Project, Universidad del Valle, 2008
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
How to retrieve images stored over large (and distributed) repositories?
Slide 13
MPEG-7 SOS: Problem Statement
M. Florian MPEG-7 Service Oriented System, Master Research Project, Universidad del Valle, 2008
http://cs.usu.edu/htm/REU-Current-Projects
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
CBIR systems have some weaknesses
Annotations: wild life, horses,
chevaux, potros …
Slide 14
MPEG-7 SOS: MPEG-7 Standard
M. Florian MPEG-7 Service Oriented System, Master Research Project, Universidad del Valle, 2008
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 15
MPEG-7 SOS: The Proposal
M. Florian MPEG-7 Service Oriented System, Master Research Project, Universidad del Valle, 2008
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Slide 16
Char Morphology
Char
Resin
Camera Microscopy
Particle Classification
1 Crassisphere
2 Inertoid
…
9 Mineroid
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
D. Chaves and M. Trujillo Impacto del Muestreo en la Clasificación de Carbonizados de Carbón, 5 CCC, 2010
Slide 17
Char Morphology: Motivation
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
Energy generation based on coal
http://www.iea.org/textbase/nppdf/free/2010/key_stats_2010.pdf http://www.worldcoal.org/coal/where-is-coal-found/
Slide 18
Char Morphology: Inherent Problems &
Proposed Approach
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
D. Chaves and M. Trujillo Impacto del Muestreo en la Clasificación de Carbonizados de Carbón, 5 CCC, 2010
Manual coal classification is a subjective and resources consuming process
Automatic Classification
Slide 19
Char Morphology: Inherent Problems &
Proposed Approach (ii)
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
D. Chaves and M. Trujillo Identificación Automática de Imágenes de Carbonizado Borrosas y con poco contenido, CONICA, 2012
Sampling and blurred or images with no content has to be considered
An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian [email protected]
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy
Slide 21
Stereo Vision
The stereo vision problem is to recover the 3D structure of the scene using
two or more images
3D Model Stereo Images
Disparity Map
Left Right
Correspondence Algorithm
Reconstruction Algorithm
Camera System
3D World
2D Images
Inverse Problem
Optics Problem
Yang Q. et al., Stereo Matching with Colour-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling, IEEE PAMI 2009
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Slide 22
Canonical Stereo Geometry and Disparity
Disparity is the distance between corresponding points
Trucco, E. and Verri A., Introductory Techniques for 3D Computer Vision, Prentice Hall 1998
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Accurate Estimation Inaccurate Estimation
P
Cr Cl
πl πr
pl pr
B
f
Z’
pr’
P ’
P
Cr Cl
πl πr
pl pr
B
f
Z
Slide 23
Ground-truth Based Evaluation
Ground-truth based evaluation is based on the comparison using disparity
ground-truth data
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Tola, E., Lepetit, V. and Fua, P., A Fast Local Descriptor for Dense Matching, CVPR 2008
Strecha, C., et al. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery, CVPR 2008
http://www.zf-usa.com/products/3d-laser-scanners/
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Slide 24
Quantitative Evaluation Methodologies
Szeliski, R., Prediction Error as a Quality Metric for Motion and Stereo, ICCV 2000
Kostliva, J., Cech, J., and Sara, R., Feasibility Boundary in Dense and Semi-Dense Stereo Matching, CVPR 2007
Tomabari, F., Mattoccia, S., and Di Stefano, L., Stereo for robots: Quantitative Evaluation of Efficient and Low-memory Dense Stereo Algorithms, ICCARV 2010
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
The use of a methodology allows to:
Assert specific components and procedures
Tune algorithm's parameters
Support decision for researchers and
practitioners
Measure the progress on the field
Slide 25
Middlebury’s Methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Select Test Bed Images Select Error Criteria
Select Error Measures
nonocc all disc
Select and Apply Stereo Algorithms
Compute Error Measures
ObjectStereo GC+SegmBorder PUTv3
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
Slide 26
Middlebury’s Methodology (ii)
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Select and Apply Stereo Algorithms
Compute Error Measures
Algorithm nonocc all disc
ObjectStereo 2.20 1 6.99 2 6.36 1
GC+SegmBorder 4.99 6 5.78 1 8.66 5
PUTv3 2.40 2 9.11 6 6.56 2
PatchMatch 2.47 3 7.80 3 7.11 3
ImproveSubPix 2.96 4 8.22 4 8.55 4
OverSegmBP 3.19 5 8.81 5 8.89 6
Algorithm Average
Rank
Final
Ranking
ObjectStereo 1.33 1
PatchMatch 3.00 2
PUTv3 3.33 3
GC+SegmBorder 4.00 4
ImproveSubPix 4.00 5
OverSegmBP 5.33 6
Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 2.20 6.99 6.36
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56
PatchMatch 2.47 7.80 7.11
ImproveSubPix 2.96 8.22 8.55
OverSegmBP 3.19 8.81 8.89
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
Slide 27
Middlebury’s Methodology (iii)
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Scharstein, D. and Szeliski, R., A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, IJCV 2002
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
Apply Evaluation Model Interpret Results
The ObjectStereo algorithm produces accurate results
Middlebury’s Evaluation Model
Algorithm Average
Rank
Final
Ranking
ObjectStereo 1.33 1
PatchMatch 3.00 2
PUTv3 3.33 3
GC+SegmBorder 4.00 4
ImproveSubPix 4.00 5
OverSegmBP 5.33 6
Slide 28
Middlebury’s Methodology (iv): Weaknesses
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
The Middlebury’s evaluation model have some shortcomings
In some cases, the ranks are assigned arbitrarily
The same average ranking does not imply the same performance (and
vice versa)
The cardinality of the set of top-performer algorithms is a free parameter
It operates values related to incommensurable measures
Slide 29
Middlebury’s Methodology (v): Weaknesses
The BMP percentage measures the quantity of disparity estimation errors
exceeding a threshold
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
The BMP measure have some shortcomings:
It is sensitive to the threshold selection
It ignores the error magnitude
It ignores the inverse relation between depth and disparity
It may conceal estimation errors of a large magnitude, and, also it may
penalise errors of small impact in the final 3D reconstruction
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
Gallup, D., et al. Variable Baseline/Resolution Stereo, CVPR, 2008
Slide 30
The A* evaluation methodology brings a theoretical background for the
comparison of stereo correspondence algorithms
The set of algorithms under evaluation
The set of estimated maps to be compared
The function that produces a vector of error measures
The set of vectors of error measures
A* Methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
Slide 31
A* Methodology (ii)
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
The evaluation model of the A* methodology addresses the comparison of
stereo correspondence algorithms as a multi-objective optimisation problem
It defines a partition over the set A (the decision space)
Subject to:
where ≺ denotes the Pareto Dominance relation:
Let p and q be two algorithms
Let Vp and Vq be a pair of vectors belonging to the objective space
Thus, three possible relations are considered
Slide 32
A* Methodology (iii): Pareto Dominance
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Van Veldhuizen, D., et al., Considerations in Engineering Parallel Multi-objective Evolutionary Algorithms, Trans in Evolutionary Computing 2003
The Pareto Dominance defines a partial order relation
VGC+SegmBorder = < 50.48, 64.90, 24.33>
VPatchMatch = < 49.95, 261.84, 32.85>
VImproveSubPix = < 50.66, 97.94, 32.01>
VGC+SegmBorder VPatchMatch
< 50.48, 64.90, 24.33> < 49.95, 261.84, 32.85>
GC+SegmBorder ~ PatchMatch
VGC+SegmBorder VImproveSubPix
< 50.48, 64.90, 24.33> < 50.66, 97.94, 32.01>
GC+SegmBorder ≺ ImproveSubPix
Slide 33
A* Methodology (iv): Illustration
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Select Test Bed Images Select Error Criteria
Select Error Measures
nonocc all disc
Select and Apply Stereo Algorithms
Compute Error Measures
ObjectStereo GC+SegmBorder PUTv3
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
Slide 34
A* Methodology (v): Illustration
The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Compute Error Measures
Algorithm nonocc all disc
ObjectStereo 2.20 6.99 6.36
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56
PatchMatch 2.47 7.80 7.11
ImproveSubPix 2.96 8.22 8.55
OverSegmBP 3.19 8.81 8.89
Algorithm nonocc all disc Set
GC+SegmBorder 50.48 64.90 24.33 A*
PatchMatch 49.95 261.84 32.85 A*
PUTv3 99.67 333.37 53.79 A’
ImproveSubPix 50.66 97.94 32.01 A’
OverSegmBP 58.65 108.60 34.58 A’
ObjectStereo 73.88 117.90 36.25 A’
Apply Evaluation Model
, GC+SegmBorder PatchMatch
ObjectStereo PUTv3 ImproveSubPix OverSegmBP , , ,
Slide 35
A* Methodology (vi): Illustration
Interpretation of results is based on the cardinality of the set A*
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Apply Evaluation Model Interpret Results
The GC+SegmBorder and the PatchMatch algorithms are, comparable among them,
and have a superior performance to the rest of algorithms
A* Evaluation Model
Algorithm nonocc all disc Set
GC+SegmBorder 50.48 64.90 24.33 A*
PatchMatch 49.95 261.84 32.85 A*
ImproveSubPix 50.66 97.94 32.01 A’
OverSegmBP 58.65 108.60 34.58 A’
ObjectStereo 73.88 117.90 36.25 A’
PUTv3 99.67 333.37 53.79 A’
Slide 36
A* Methodology (vii): Strength and Weakness
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
Strength: It allows a formal interpretation of results, based on the cardinality
of the set A*, and in regard to considered imagery test-bed
Weakness: It does not allow an exhaustive evaluation of the entire set of
algorithms under evaluation
It computes the set A* just once, and does not bring information about A’
Slide 37
A* Groups Methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
It extends the evaluation model of the A* methodology, incorporating the
capability of performing an exhaustive evaluation
It introduces the partitioningAndGrouping algorithm
A = Set ( { } );
A.load( “Algorithms.dat” );
A* = Set ( { } );
A’ = Set ( { } );
group = 1;
do {
computePartition( A, A*, A’, g, ≺ );
A*.save ( “A*_group_”+group );
group++;
A.update ( A’ ); // A = A / A*
A*.removeAll ( ); // A* = { }
A’.removeAll ( ); // A’ = { }
}while ( ! A.isEmpty ( ) );
subject to:
Slide 38
The A* Groups methodology uses the Sigma-Z-Error
(SZE) measure
The SZE measure has the following properties:
It is inherently related to depth reconstruction in a stereo system
It is based on the inverse relation between depth and disparity
It considers the magnitude of the estimation error
It is threshold free
A* Groups Methodology (ii): Sigma-Z-Error
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
Slide 39
A* Groups Methodology (iii): Illustration
The evaluation process of selected algorithms by using the proposal
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Select Test Bed Images Select Error Criteria
Select Error Measures
nonocc all disc
Select and Apply Stereo Algorithms
Compute Error Measures
ObjectStereo GC+SegmBorder PUTv3
PatchMatch ImproveSubPix OverSegmBP
Slide 40
A* Groups Methodology (iv): Illustration
The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Compute Error Measures
Algorithm nonocc all disc
ObjectStereo 73.88 117.90 36.25
GC+SegmBorder 50.48 64.90 24.33
PUTv3 2.40 9.11 6.56
PatchMatch 49.95 261.84 32.85
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58
, GC+SegmBorder PatchMatch
ObjectStereo PUTv3 ImproveSubPix OverSegmBP , , ,
Algorithm nonocc all disc Group
GC+SegmBorder 50.48 64.90 24.33 1
PatchMatch 49.95 261.84 32.85 1
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25
Apply Evaluation Model
,
Slide 41
A* Groups Methodology (v): Illustration
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
ObjectStereo PUTv3 ImproveSubPix OverSegmBP , , ,
Algorithm nonocc all disc
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25
Apply Evaluation Model
Algorithm nonocc all disc Group
ImproveSubPix 50.66 97.94 32.01 2
PUTv3 99.67 333.37 53.79
ObjectStereo 73.88 117.90 36.25
OverSegmBP 58.65 108.60 34.58
ImproveSubPix
ObjectStereo PUTv3 OverSegmBP , ,
ObjectStereo PUTv3 OverSegmBP , ,
Algorithm nonocc all disc
PUTv3 99.67 333.37 53.79
OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25
ObjectStereo
OverSegmBP
, PUTv3
Algorithm nonocc all disc Group
OverSegmBP 58.65 108.60 34.58 3
PUTv3 99.67 333.37 53.79
ObjectStereo 73.88 117.90 36.25
And so on …
Slide 42
A* Groups Methodology (vi): Illustration
Interpretation of results is based on the cardinality of each group
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Apply Evaluation Model Interpret Results
There are 5 groups of different performance
The GC+SegmBorder and the PatchMatch algorithms are, comparable among them,
and have a superior performance to the rest of algorithms
The ImproveSubPix algorithm is superior to
the OverSegmBP, the ObjectStereo, and the PUTv3 algorithms
…
The PUTv3 algorithm has the lowest performance
A* Groups Evaluation Model
Algorithm nonocc all disc Group
GC+SegmBorder 50.48 64.90 24.33 1
PatchMatch 49.95 261.84 32.85 1
ImproveSubPix 50.66 97.94 32.01 2
OverSegmBP 58.65 108.60 34.58 3
ObjectStereo 73.88 117.90 36.25 4
PUTv3 99.67 333.37 53.79 5
Slide 43
Experimental Results
The conducted evaluation involves the following elements:
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Test Bed Images
Error Criteria
Evaluation Models
Error Measures
A* Groups Middlebury
SZE , BMP
nonocc , all , disc
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
Stereo Algorithms 112 algorithms from the Middlebury’s repository
Slide 44
Experimental Results (ii)
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
Algorithm Strategy Group Middlebury’s
Ranking
DoubleBP Global 1 4
PatchMatch Local 1 11
GC+SegmBorder Global 1 13
FeatureGC Global 1 18
Segm+Visib Global 1 29
MultiresGC Global 1 30
DistinctSM Local 1 34
GC+occ Global 1 67
MultiCamGC Global 1 68
Algorithm Group Middlebury’s
Ranking
ADCensus 2 1
AdaptingBP 2 2
CoopRegion 2 3
DoubleBP 1 4
RDP 2 5
OutlierConf 2 6
SubPixDoubleBP 2 7
SurfaceStereo 2 8
WarpMat 2 9
ObjectStereo 2 10
PatchMatch 1 11
Undr+OverSeg 2 12
GC+SegmBorder 1 13
InfoPermeable 2 14
CostFilter 2 15
Slide 45
Conclusions
The use of the A* Groups methodology allows to perform an exhaustive
evaluation, as well as an objective interpretation of results
Innovative results in regard to the comparison of stereo correspondence
algorithms were obtained using proposed methodology and the SZE error
measure
The introduced methodology offers advantages over the conventional
approaches to compare stereo correspondence algorithms
Authors are already working in order to provide to the research community an
accessible way to use the introduced methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy
An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian [email protected]
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy
Slide 47
Final Remarks
More information about the MMV-Lab can be found at
http://www.slideshare.net/mmv-lab-univalle
We are looking forward to create bounds with international collaborators
We invite you to participate at the 4th Latin American Conference on
Networked and Electronic Media, LACNEM, in Chile next October
If you have any question or concern please do not hesitate to contact me
[email protected] / www.ivancabezas.com
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects
MMV Research Laboratory: A
Retrospective Around Multimedia and
Computer Vision Projects Ivan Cabezas
July 18th 2012
Universidad Señor de Sipán – Chiclayo, Peru
Top Related