Using multi-modal 3D contours and their relations for vision and robotics

Journal of Visual Communication and Image Representation

Volumn 21, Issue 8, 2010, Pages 850-864

  Baeski, Emre ^a   Pugeault, Nicolas ^b   Kalkan, Sinan ^c   Bodenhagen, Leon ^a   Piater, Justus H ^d   Krüger, Norbert ^a  

^a UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

^d UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

3D contours; 3D reasoning; Cognitive vision; Contour relations; Contour representation; Driver assistance; Grasping; Perceptual relations

Indexed keywords

3D CONTOURS; 3D REASONING; COGNITIVE VISION; CONTOUR RELATIONS; CONTOUR REPRESENTATION; DRIVER ASSISTANCE; GRASPING; PERCEPTUAL RELATIONS;

AUTOMOBILE DRIVERS; COMPUTER VISION; ROBOTICS; ROBOTS;

THREE DIMENSIONAL;

EID: 77958463951     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2010.06.006     Document Type: Article

References (71)

1. O.I. Camps, T. Kanungo, Hierarchical organization of appearance-based parts and relations for object recognition, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, IEEE, 1998, pp. 685-691.
2. B. Leibe, B. Schiele, Analyzing appearance and contour-based methods for object categorization, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPRG-03), 2003, pp. 409-415.
3. A. Sha'asua, S. Ullman, Structural saliency: the detection of globally salient structures using a locally connected network, in: Second International Conference on Computer Vision, 1988, pp. 321-327.
4. J. Shotton, A. Blake, and R. Cipolla Multiscale categorical object recognition using contour fragments IEEE Trans. Pattern Anal. Mach. Intell. 30 2008 1270 1281
5. D.G. Lowe Perceptual Organization and Visual Recognition 1985 Kluwer Academic Publishers.
6. S. Ullman High-level Vision 1996 MIT Press
7. D.G. Lowe Distinctive image features from scale-invariant keypoints Int. J. Comput. Vision 2 2004 91 110
8. K. Mykolajczyk, and C. Schmid An affine invariant interest point detector Proceedings of the European Conference in Computer Vision (ECCV 2002) 2002 Springer-Verlag
9. K. Mikolajczyk, and C. Schmid A performance evaluation of local descriptors IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 1615 1630
10. T. Quack, V. Ferrari, B. Leibe, L. Van Gool, Efficient mining of frequent and distinctive feature configurations, in: ICCV07, 2007, pp. 1-8.
11. A. Mian, M. Bennamoun, and R. Owens A novel representation and feature matching algorithm for automatic pairwise registration of range images Int. J. Comput. Vision 66 2006 19 40
12. J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, in: British Machine Vision Conference, vol. 1, 2002, pp. 384-393.
13. P. Moreels, P. Perona, Evaluation of features detectors and descriptors based on 3D objects, in: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol. 1, 2005, pp. 800-807.
14. C. Altmann, H. Bülthoff, and Z. Kourtzi Perceptual organization of local elements into global shapes in the human visual cortex Curr. Biol. 13 2003 342 349
15. D.J. Field, A. Hayes, and R.F. Hess Contour integration by the human visual system: evidence for a local association field Vision Res. 33 1993 173 193
16. M. Behrmann, and R. Kimchi What does visual agnosia tell us about perceptual organization and its relationship to object perception? J. Exp. Psychol. Human Percept. Perform. 29 2003 19 42
17. J.D. Winter, and J. Wagemans Contour-based object identification and segmentation: stimuli, norms and data, and software tools Behav. Res. Methods Instrum. Comput. 36 2004 604 624
18. Y. Lerner, T. Hendler, D. Ben-Bashat, M. Harel, and R. Malach A hierarchical axis of object processing stages in the human visual cortex Cereb Cortex 11 2001 287 297
19. K. Tanaka Mechanisms of visual object recognition: monkey and human studies Curr. Opin. Neurobiol. 7 1997 523 529
20. M. Kikuchi, K. Sakai, and Y. Hirai The mechanism of 3D contour perception J. Vis. 5 2005 76
21. X. Wang, J. Keller, P. Gader, Using spatial relationships as features in object recognition, Fuzzy Information Processing Society, 1997, NAFIPS '97, 1997 Annual Meeting of the North American, 1997, pp. 160-165.
22. O. Henricsson Inferring homogeneous regions from rich image attributes Automatic Extraction of Man-Made Objects from Aerial and Space Images 1995 Birkhuser Verlag 13 22
23. S. Dickinson, A. Pentland, A. Rosenfeld, From volumes to views: an approach to 3D object recognition, Automated CAD-Based Vision, Workshop on Directions in (1991), 1991, pp. 85-96.
24. I. Biederman Recognition-by-components: a theory of human image understanding Psychol. Rev. 94 1987 115 147
25. L.G. Shapiro, J.D. Moriarty, R.M. Haralick, and P.G. Mulgaonkar Matching 3D objects using a relational paradigm Pattern Recognit. 17 1984 385 405
26. S. Fidler, G. Berginc, A. Leonardis, Hierarchical statistical learning of generic parts of object structure, in: CVPR06, 2006, pp. 182-189.
27. S. Fidler, A. Leonardis, Towards scalable representations of object categories: learning a hierarchy of parts, in: CVPR07, 2007.
28. S. Fidler, M. Boben, A. Leonardis, Similarity-based cross-layered hierarchical representation for object categorization, in: CVPR08, 2008, pp. 182-189.
29. J. Ponce, D. Stam, and B. Faverjon On computing two-finger force-closure grasps of curved 2D objects Int. J. Rob. Res. 12 1993 263 273
30. J. Speth, A. Morales, P. Sanz, Vision-based grasp planning of 3D objects by extending 2D contour based algorithms, in: Intelligent Robots and Systems, IROS 2008, IEEE/RSJ International Conference on, 2008, pp. 2240-2245.
31. N. Bergström, J. Bohg, D. Kragic, Integration of visual cues for robotic grasping, in: ICVS, 2009, pp. 245-254.
32. N. Krüger, M. Lappe, and F. Wörgötter Biologically motivated multi-modal processing of visual primitives Interdiscipl. J. Artif. Intell. Simul. Behav. 1 2004 417 428
33. N. Pugeault, F. Wörgötter, N. Krüger, Visual primitives: local, condensed, and semantically rich visual descriptors and their applications in robotics, Int. J. Humanoid Rob. (accepted for publication).
34. J. Clarke, Modeling uncertainty: a primer, Technical Report 2161/98, University of Oxford, Dept. Engineering Science, 1998.
35. N. Pugeault, S. Kalkan, E. Baeski, F. Wörgötter, N. Krüger, Relations between reconstructed 3D entities, in: Proceedings of Int. Conf. on Computer Vision Theory and Applications (VISAPP'08), 2008.
36. N. Pugeault, F. Wörgötter, N. Krüger, Multi-modal scene reconstruction using perceptual grouping constraints, in: Proc. IEEE Workshop on Perceptual Organization in Computer Vision (in conjunction with CVPR'06), 2006.
37. L. Piegl, and W. Tiller The NURBS Book 1995 Springer-Verlag London, UK
38. I.-K. Lee Curve reconstruction from unorganized points Comput. Aided Geom. Des. 17 2000 161 177
39. J. Hoschek, and D. Lasser Fundamentals of Computer Aided Geometric Design 1993 A.K. Peters, Ltd.
40. D.R. Murray, Patchlets: a method of interpreting correlation stereo three-dimensional data, Ph.D. thesis, 2004.
41. D. Murray, J.J. Little, Patchlets: Representing stereo vision data with surface elements, in: WACV-MOTION '05: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05), vol. 1, 2005, pp. 192-199.
42. J. Karhunen, L. Wang, R. Vigario, Nonlinear PCA type approaches for source separation and independent component analysis, in: Proc. ICNN, 1995, pp. 995-1000.
43. M. Tkalcic, and J. Tasic Colour spaces: perceptual, historical and applicational background, EUROCON 2003, Computer as a Tool IEEE Region 8 1 2003 304 308
44. R. Hunt Measuring Colour 3rd ed. 1998 Fountain Press Kingston-upon-Thames
45. B. Boesman, L. Jensen, E. Baeski, N. Pugeault, N. Krüger, Bayesian reasoning using 3D relations for lane marker detection, in: Vision, Modeling, and Visualization Workshop (VMVG-09), 2009.
46. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann Publishers, Inc.
47. M. Bertozzi, A. Broggi, and A. Fascioli Vision-based intelligent vehicles: state of the art and perspectives Rob. Auton. Syst. 32 2000 1 16
48. J.C. McCall, and M.M. Trivedi Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation IEEE Trans. Intell. Transp. Syst. 7 2006 20 37
49. Y. Wang, E. Teoh, and D. Shen Lane detection and tracking using b-snake Image Vision Comput. 22 2004 269 280
50. S. Nedevschi, R. Schmidt, T. Graf, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, 3D lane detection system based on stereovision, in: IEEE Intelligent Transportation Systems Conference, 2004, pp. 161-166.
51. S. Kalkan, F. Wörgötter, N. Krüger, Depth prediction at homogeneous image structures, in: VISAPP'08: Int. Conference on Computer Vision Theory and Applications, 2008.
52. S. Kalkan, F. Wörgötter, and N. Krüger First-order and second-order statistical analysis of 3D and 2D structure Network Comput. Neural Syst. 18 2007 129 160
53. S.P. Sabatini, G. Gastaldi, F. Solari, J. Diaz, E. Ros, K. Pauwels, K.M.M.V. Hulle, N. Pugeault, and N. Krüger Compact and accurate early vision processing in the harmonic space Int. Conf. Comput. Vision Theory Appl. (VISAPP) 1 2007 213 220
54. J. Ralli, J. Diaz, S. Kalkan, N. Krüger, E. Ros, Disparity disambiguation by fusion of signal- and symbolic-level information, Machine Vision and Aplications (in press).
55. M. Popović, D. Kraft, L. Bodenhagen, E. Baeski, N. Pugeault, D. Kragic, T. Asfour, and N. Krüger A strategy for grasping unknown objects based on co-planarity and colour information Rob. Auton. Syst. 58 2010 551 565
56. A. Saxena, J. Driemeyer, and A.Y. Ng Robotic grasping of novel objects using vision Int. J. Rob. Res. 27 2008 157 173 (Pubitemid 351169712)
57. I. Kamon, T. Flash, S. Edelman, Learning to grasp using visual information, in: Proceedings of the 1996 IEEE International Conference on Robotics and Automation, 1996, pp. 2470-2476.
58. K. Huebner, S. Ruthotto, D. Kragic, Minimum volume bounding box decomposition for shape approximation in robot grasping, in: Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008, pp. 1628-1633.
59. L. Bodenhagen, Adaptive Grasping based on Second Order Visual Feature Relations, Master's thesis, University of Southern Denmark, 2009.
60. N. Pugeault, F. Wörgötter, N. Krüger, Accumulated visual representation for cognitive vision, in: Proceedings of the British Machine Vision Conference (BMVC), 2008.
61. F. Pilz, N. Pugeault, N. Kruger, Comparison of point and line features and their combination for rigid body motion estimation, in: Statistical and Geometrical Approaches to Visual Motion Analysis, LNCS, vol. 5604, Springer, 2009, pp. 280-304.
62. B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon Bundle adjustment - a modern synthesis W. Triggs, A. Zisserman, R. Szeliski, Vision Algorithms: Theory and Practice 2000 LNCS, Springer-Verlag 298 375
63. P. Dissanayake, P. Newman, H. Durrant-Whyte, S. Clark, and M. Csorba A solution to the simultaneous localization and mapping (SLAM) problem IEEE Trans. Rob. Autom. 17 2001 229 241 (Pubitemid 32829731)
64. R. van der Merwe, N. de Freitas, A. Doucet, E. Wan, The unscented particle filter, in: Advances in Neural Information Processing Systems (NIPS), vol. 13, 2000.
65. J. Guivant, and E. Nebot Optimization of the simultaneous localization and map-building algorithm for real-time implementation IEEE Trans. Rob. Autom. 17 2001 242 257
66. S. Thrun, Y. Liu, D. Koller, A. Ng, Z. Ghahramani, and H. Durrant-Whyte Simultaneous localization and mapping with sparse extended information filters Int. J. Rob. Res. 23 2004 693 716
67. T. Lemaire, C. Berger, I.-K. Jung, and S. Lacroix Vision-based SLAM: stereo and monocular approaches Int. J. Comput. Vision 74 2007 343 364
68. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit, FastSLAM: a factored solution to the simultaneous localization and mapping problem, in: Proceedings of the AAAI National Conference on Artificial Intelligence, 2002.
69. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit, FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges, in: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), 2003.
70. R. Montúfar-Chaveznava, M. Pérez-Mesa, I. Caldelas, The fly algorithm for robot navigation, in: Research and Education in Robotics - EUROBOT 2008, Communications in Computer and Information Science, Springer Berlin Heidelberg, 2009, pp. 119-127.
71. M. Isard, and A. Blake Condensation - conditional density propagation for visual tracking IJCV 29 1998 5 28