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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
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
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
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
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
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
Industrial Collaborations




MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects   Slide 7
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Middlebury’s Methodology (ii)

        Select and Apply Stereo Algorithms                                                  Apply Evaluation Model

                                                                                     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
                Compute Error Measures                                                 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
            Algorithm            nonocc           all        disc
                                                                                    OverSegmBP             3.19   5        8.81   5   8.89   6
           ObjectStereo             2.20         6.99        6.36
        GC+SegmBorder               4.99         5.78        8.66
               PUTv3                2.40         9.11        6.56                       Algorithm          Average           Final
           PatchMatch               2.47         7.80        7.11                                           Rank            Ranking
         ImproveSubPix              2.96         8.22        8.55                     ObjectStereo            1.33                1
          OverSegmBP                3.19         8.81        8.89                      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., http://vision.middlebury.edu/stereo/eval/, 2012
                               An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy     Slide 26
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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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
  • 7. Industrial Collaborations MMV Research Laboratory: A Retrospective Around Multimedia and Computer Vision Projects Slide 7
  • 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
  • 26. Middlebury’s Methodology (ii) Select and Apply Stereo Algorithms Apply Evaluation Model 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 Compute Error Measures 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 Algorithm nonocc all disc OverSegmBP 3.19 5 8.81 5 8.89 6 ObjectStereo 2.20 6.99 6.36 GC+SegmBorder 4.99 5.78 8.66 PUTv3 2.40 9.11 6.56 Algorithm Average Final PatchMatch 2.47 7.80 7.11 Rank Ranking ImproveSubPix 2.96 8.22 8.55 ObjectStereo 1.33 1 OverSegmBP 3.19 8.81 8.89 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., http://vision.middlebury.edu/stereo/eval/, 2012 An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 26
  • 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