A brief retrospective of selected projects elaborated at the Multimedia and Vision Laboratory in the Universidad del Valle. This talk was presented by teleconference to Universidad Señor de Sipán, Peru.
MMV Research Laboratory :A Retrospective Around Multimedia and Computer Vision Projects
1. MMV Research Laboratory: A
Retrospective Around Multimedia and
Computer Vision Projects
Ivan Cabezas
ivan.cabezas@correounivalle.edu.co
July 18th 2012
Universidad Señor de Sipán – Chiclayo, Peru
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 2
3. The Universidad del Valle
The Universidad del Valle is the largest university in the south west of
Colombia
http://www.univalle.edu.co
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 3
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)
http://www.univalle.edu.co
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 4
5. Multimedia and Vision Laboratory
MMV is a multidisciplinary research group of the EISC
Meetings, 2007 & 2011
INTERACTIVIA, 2009
Maria at UNAL Ivan at WAC
2011 2012
LACNEM, 2009
http://www.lacnem.org
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 5
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 6
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 8
9. Research Interests
Multimedia and Computer Vision
Images
Computing
System
Information
Computer Vision
http://www.slideshare.net/mmv-lab-univalle http://vision.mas.ecp.fr/Personnel/teboul/index.php/
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 9
10. A Camera Model
A Camera is a sensor following a model
3D World
Camera
System
2D Images
http://www.univalle.edu.co
http://quarknet.fnal.gov/fnal-uc/quarknet-summer-research/QNET2010/Astronomy/ http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 10
11. Some Research Projects
MPEG-7 UV
Prokaryota
Clusters: Espacial +
K - Means
MPEG-7 SOS Vitisoft
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 11
12. MPEG-7 SOS: Motivation
How to retrieve images stored over large (and distributed) repositories?
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 Slide 12
13. MPEG-7 SOS: Problem Statement
CBIR systems have some weaknesses
Annotations: wild life, horses,
chevaux, potros …
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 Slide 13
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 14
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 15
16. Char Morphology
Char
Resin
Particle Classification
1 Crassisphere
2 Inertoid
…
9 Mineroid
Microscopy Camera
D. Chaves and M. Trujillo Impacto del Muestreo en la Clasificación de Carbonizados de Carbón, 5 CCC, 2010
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 16
17. Char Morphology: Motivation
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/
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 17
18. Char Morphology: Inherent Problems &
Proposed Approach
Manual coal classification is a subjective and resources consuming process
Automatic Classification
D. Chaves and M. Trujillo Impacto del Muestreo en la Clasificación de Carbonizados de Carbón, 5 CCC, 2010
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 18
19. Char Morphology: Inherent Problems &
Proposed Approach (ii)
Sampling and blurred or images with no content has to be considered
D. Chaves and M. Trujillo Identificación Automática de Imágenes de Carbonizado Borrosas y con poco contenido, CONICA, 2012
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 19
20. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy
21. Stereo Vision
The stereo vision problem is to recover the 3D structure of the scene using
two or more images
3D World
Optics
Problem
Camera Inverse
System Problem
Disparity Map Reconstruction
Algorithm
2D Images
Left Right
Correspondence
Stereo Images Algorithm 3D Model
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 21
22. Canonical Stereo Geometry and Disparity
Disparity is the distance between corresponding points
Accurate Estimation Inaccurate Estimation
P P
P’
Z Z’
pl pr pl pr
πl πr πl πr
pr ’
f f
Cl B Cr Cl B Cr
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 Slide 22
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 23
24. Quantitative Evaluation Methodologies
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
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 Slide 24
25. Middlebury’s Methodology
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 25
27. Middlebury’s Methodology (iii)
Apply Evaluation Model Interpret Results
The ObjectStereo algorithm
produces accurate results
Middlebury’s
Evaluation Model
Algorithm Average Final
Rank 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
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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 27
28. Middlebury’s Methodology (iv): Weaknesses
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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 28
29. Middlebury’s Methodology (v): Weaknesses
The BMP percentage measures the quantity of disparity estimation errors
exceeding a threshold
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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 29
30. A* Methodology
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
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 Slide 30
31. A* Methodology (ii)
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
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 Slide 31
32. A* Methodology (iii): Pareto Dominance
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
Van Veldhuizen, D., et al., Considerations in Engineering Parallel Multi-objective Evolutionary Algorithms, Trans in Evolutionary Computing 2003
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 32
33. A* Methodology (iv): Illustration
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 33
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
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 2.20 6.99 6.36
GC+SegmBorder , PatchMatch
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56 ObjectStereo , PUTv3 , ImproveSubPix , OverSegmBP
PatchMatch 2.47 7.80 7.11
ImproveSubPix 2.96 8.22 8.55 Algorithm nonocc all disc Set
OverSegmBP 3.19 8.81 8.89 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’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 34
35. A* Methodology (vi): Illustration
Interpretation of results is based on the cardinality of the set A*
Apply Evaluation Model Interpret Results
A* Evaluation Model
The GC+SegmBorder and the PatchMatch
Algorithm nonocc all disc Set
algorithms are, comparable among them,
and have a superior performance to the rest
GC+SegmBorder 50.48 64.90 24.33 A* of algorithms
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’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 35
36. A* Methodology (vii): Strength and Weakness
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’
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 Slide 36
37. A* Groups Methodology
It extends the evaluation model of the A* methodology, incorporating the
capability of performing an exhaustive evaluation
subject to:
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 ( ) );
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 37
38. A* Groups Methodology (ii): Sigma-Z-Error
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
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 38
39. A* Groups Methodology (iii): Illustration
The evaluation process of selected algorithms by using the proposal
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 39
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
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 73.88 117.90 36.25
GC+SegmBorder ,, PatchMatch
GC+SegmBorder 50.48 64.90 24.33
PUTv3 2.40 9.11 6.56 ObjectStereo , PUTv3 , ImproveSubPix , OverSegmBP
PatchMatch 49.95 261.84 32.85
Algorithm nonocc all disc Group
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58 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
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 40
41. A* Groups Methodology (v): Illustration
Apply Evaluation Model
ObjectStereo , PUTv3, ImproveSubPix , OverSegmBP
Algorithm nonocc all disc
ObjectStereo, PUTv3 , OverSegmBP
PUTv3 99.67 333.37 53.79 Algorithm nonocc all disc
ImproveSubPix 50.66 97.94 32.01 PUTv3 99.67 333.37 53.79
OverSegmBP 58.65 108.60 34.58 OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25 ObjectStereo 73.88 117.90 36.25
OverSegmBP
ImproveSubPix
PUTv3 , ObjectStereo
ObjectStereo , PUTv3 , OverSegmBP
Algorithm nonocc all disc Group
Algorithm nonocc all disc Group OverSegmBP 58.65 108.60 34.58 3
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01 2
ObjectStereo 73.88 117.90 36.25
PUTv3 99.67 333.37 53.79
ObjectStereo 73.88 117.90 36.25
OverSegmBP 58.65 108.60 34.58
And so on …
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 41
42. A* Groups Methodology (vi): Illustration
Interpretation of results is based on the cardinality of each group
Apply Evaluation Model Interpret Results
A* Groups
Evaluation Model
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
Algorithm nonocc all disc Group of algorithms
GC+SegmBorder 50.48 64.90 24.33 1
The ImproveSubPix algorithm is superior to
PatchMatch 49.95 261.84 32.85 1 the OverSegmBP, the ObjectStereo, and
ImproveSubPix 50.66 97.94 32.01 2 the PUTv3 algorithms
OverSegmBP 58.65 108.60 34.58 3
…
ObjectStereo 73.88 117.90 36.25 4
PUTv3 99.67 333.37 53.79 5 The PUTv3 algorithm has the lowest
performance
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 42
43. Experimental Results
The conducted evaluation involves the following elements:
Test Bed Images
Error Criteria nonocc , all , disc
Error Measures SZE , BMP
Stereo Algorithms 112 algorithms from the Middlebury’s repository
Evaluation Models A* Groups Middlebury
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 43
44. Experimental Results (ii)
Algorithm Group Middlebury’s
Ranking
ADCensus 2 1
AdaptingBP 2 2
CoopRegion 2 3
DoubleBP 1 4
RDP 2 5
OutlierConf 2 6
Algorithm Strategy Group Middlebury’s SubPixDoubleBP 2 7
Ranking SurfaceStereo 2 8
DoubleBP Global 1 4 WarpMat 2 9
PatchMatch Local 1 11 ObjectStereo 2 10
GC+SegmBorder Global 1 13 PatchMatch 1 11
FeatureGC Global 1 18 Undr+OverSeg 2 12
Segm+Visib Global 1 29 GC+SegmBorder 1 13
MultiresGC Global 1 30 InfoPermeable 2 14
DistinctSM Local 1 34 CostFilter 2 15
GC+occ Global 1 67
MultiCamGC Global 1 68
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 44
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 Slide 45
46. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy
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
ivan.cabezas@correounivalle.edu.co / www.ivancabezas.com
MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 47
48. MMV Research Laboratory: A
Retrospective Around Multimedia and
Computer Vision Projects
Ivan Cabezas
ivan.cabezas@correounivalle.edu.co
July 18th 2012
Universidad Señor de Sipán – Chiclayo, Peru