Detecting, segmenting and tracking unknown objects using multi-label MRF inference

Computer Vision and Image Understanding

Volumn 118, Issue , 2014, Pages 111-127

  Björkman, Mårten ^a   Bergström, Niklas ^a   Kragic, Danica ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Active perception; Figure ground segmentation; GPU acceleration; MRF; Multi object tracking; Object detection

Indexed keywords

ACTIVE PERCEPTIONS; FIGURE-GROUND SEGMENTATIONS; GPU ACCELERATIONS; MRF; MULTI-OBJECT TRACKING; OBJECT DETECTION;

PROGRAM PROCESSORS; THREE DIMENSIONAL;

IMAGE SEGMENTATION;

EID: 84890998700     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2013.10.007     Document Type: Article

References (77)

1. L. Itti, C. Koch, and E. Niebur A model of saliency-based visual attention for rapid scene analysis IEEE Trans. Pattern Anal. Mach. Intell. 20 11 1998 1254 1259
2. Y. Boykov, M.-P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, in: Proc. IEEE Int. Conf. Comput. Vis., vol. 1, 2001, pp. 105-112.
3. C. Rother, V. Kolmogorov, and A. Blake Grabcut: interactive foreground extraction using iterated graph cuts ACM Trans. Graph. 23 2004 309 314
4. A. Mishra, Y. Aloimonos, Visual segmentation of simple objects for robots, in: Proc. Robotics: Sci. Syst., 2011, pp. 217-224.
5. P. Fitzpatrick, and G. Metta Grounding vision through experimental manipulation Philos. Trans. Roy. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 361 1811 2003 2165 2185
6. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor Learning object affordances: from sensory-motor coordination to imitation IEEE Trans. Robot. 24 1 2008 15 26
7. C. Bibby, I. Reid, Robust real-time visual tracking using pixel-wise posteriors, in: Proc. Eur. Conf. Comput. Vis., 2008, pp. 831-844.
8. P. Chockalingam, N. Pradeep, S. Birchfield, Adaptive fragments-based tracking of non-rigid objects using level sets, in: Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 1530-1537.
9. Y. Shi, W. Karl, Real-time tracking using level sets, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2, 2005, pp. 34-41.
10. L. Grady, and C. Alvino The piecewise smooth Mumford-Shah functional on an arbitrary graph IEEE Trans. Image Process. 18 11 2009 2547 2561
11. H. Sidenbladh, M.J. Black, D.J. Fleet, Stochastic tracking of 3d human figures using 2d image motion, in: Proc. Eur. Conf. Comput. Vis., 2000, pp. 702-718.
12. M. de La Gorce, N. Paragios, D.J. Fleet, Model-based hand tracking with texture, shading and self-occlusions, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2008, pp. 1-8.
13. D. Koller, K. Daniilidis, and H. Nagel Model-based object tracking in monocular image sequences of road traffic scenes Int. J. Comput. Vis. 10 3 1993 257 281
14. B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool Coupled object detection and tracking from static cameras and moving vehicles IEEE Trans. Pattern Anal. Mach. Intell. 30 10 2008 1683 1698
15. M. Kass, A. Witkin, and D. Terzopoulos Snakes: active contour models Int. J. Comput. Vis. 1 4 1988 321 331
16. V. Caselles, F. Catté, T. Coll, and F. Dibos A geometric model for active contours in image processing Numer. Math. 66 1993 1 31
17. D. Mumford, and J. Shah Optimal approximations by piecewise smooth functions and associated variational problems Commun. Pure Appl. Math. 42 5 1989 577 685
18. T. Chan, and L. Vese Active contours without edges IEEE Trans. Image Process. 10 2 2001 266 277
19. T. Chan, W. Zhu, Level set based shape prior segmentation, in: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2, 2005, pp. 1164-1170.
20. D. Cremers, F. Tischhäuser, J. Weickert, and C. Schnörr Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional Int. J. Comput. Vis. 50 2002 295 313
21. D. Cremers, and G. Funka-Lea Dynamical statistical shape priors for level set based tracking IEEE Trans. Pattern Anal. Mach. Intell. 28 2006 1262 1273
22. D. Freedman, and T. Zhang Active contours for tracking distributions IEEE Trans. Image Process. 13 4 2004 518 526
23. J. Besag On the statistical analysis of dirty pictures J. Royal Stat. Soc. B-48 1986 259 302
24. L. Ford, and D. Fulkerson Maximal flow through a network Can. J. Math. 8 1956 399 404
25. Y. Boykov, O. Veksler, and R. Zabih Fast approximate energy minimization via graph cuts IEEE Trans. Pattern Anal. Mach. Intell. 23 11 2001 1222 1239
26. M. Wainwright, T. Jaakkola, and A. Willsky Map estimation via agreement on (hyper)trees: message-passing and linear programming approaches IEEE Trans. Inf. Theor. 51 2002 3697 3717
27. V. Kolmogorov Convergent tree-reweighted message passing for energy minimization IEEE Trans. Pattern Anal. Mach. Intell. 2006 1568 1583
28. J. Pearl, Reverend Bayes on inference engines: a distributed hierarchical approach, in: Proc. Natl. Conf. Artif. Intell., 1982, pp. 133-136.
29. Y. Weiss Correctness of local probability propagation in graphical models with loops Neural Comput. 12 1 2000 1 41
30. N. Komodakis, G. Tziritas, N. Paragios, Fast, approximately optimal solutions for single and dynamic MRFs, in: Proc. Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
31. M.F. Tappen, W.T. Freeman, Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters, in: Proc. IEEE Int. Conf. Comput. Vis., 2003, pp. 900-907.
32. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother A comparative study of energy minimization methods for Markov random fields with smoothness-based priors IEEE Trans. Pattern Anal. Mach. Intell. 30 6 2008 1068 1080
33. Y. Boykov, and V. Kolmogorov An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision IEEE Trans. Pattern Anal. Mach. Intell. 26 9 2004 1124 1137
34. D. Batra, A. Kowdle, D. Parikh, J. Luo, T. Chen, iCoseg: interactive co-segmentation with intelligent scribble guidance., in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 3169-3176.
35. S. Vicente, V. Kolmogorov, C. Rother, Joint optimization of segmentation and appearance models, in: Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 755-762.
36. A. Mishra, Y. Aloimonos, Active segmentation with fixation, in: Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 468-475.
37. S. Bagon, O. Boiman, M. Irani, What is a good image segment? A unified approach to segment extraction, in: Proc. Eur. Conf. Comput. Vis., 2008, pp. 30-44.
38. A. Blake, C. Rother, M. Brown, P. Perez, P. Torr, Interactive image segmentation using an adaptive GMMRF model, in: Proc. Eur. Conf. Comput. Vis., 2004, pp. 428-441.
39. Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum Lazy snapping ACM Trans. Graph. 23 2004 303 308
40. A.M. Treisman, and G. Gelade A feature-integration theory of attention Cogn. Psychol. 12 1 1980 97 136
41. J.M. Wolfe, K.R. Cave, and S.L. Franzel Guided search: an alternative to the feature integration model for visual search. J. Exp. Psychol.: Human Percept. Perform. 15 3 1989 419
42. C. Koch, and S. Ullman Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiol. 4 4 1985 219 227
43. J.K. Tsotsos, S.M. Culhane, W.Y. Kei Wai, Y. Lai, N. Davis, and F. Nuflo Modeling visual attention via selective tuning Artif. Intell. 78 1 1995 507 545
44. J.J. Clark, N.J. Ferrier, Modal control of an attentive vision system, in: Proc. IEEE Int. Conf. Comput. Vis., 1988, pp. 514-523.
45. A. Oliva, A. Torralba, M. Castelhano, J. Henderson, Top-down control of visual attention in object detection, in: Proc. IEEE Int. Conf. Image Process., vol. 1, 2003, pp. 253-256.
46. G. Kootstra, N. Bergström, D. Kragic, Using symmetry to select fixation points for segmentation, in: Proc. IEEE Int. Conf. Pattern Recognit., 2010, pp. 3894-3897.
47. D. Martin, C. Fowlkes, and J. Malik Learning to detect natural image boundaries using local brightness, color, and texture cues IEEE Trans. Pattern Anal. Mach. Intell. 26 5 2004 530 549
48. Y. Zhang, M. Brady, and S. Smith Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm IEEE Trans. Med. Imaging 20 1 2001 45 57
49. R. Potts Some generalized order-disorder transformation Proc. Camb. Philos. Soc. 48 1952 106 109
50. D. Geman, S. Geman, C. Graffigne, and P. Dong Boundary detection by constrained optimization IEEE Trans. Pattern Anal. Mach. Intell. 12 7 1990 609 628
51. P. Kohli, L. Ladický, and P.H. Torr Robust higher order potentials for enforcing label consistency Int. J. Comput. Vis. 82 3 2009 302 324
52. N. Vasconcelos, A. Lippman, Empirical Bayesian EM-based motion segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Los Alamitos, CA, USA, 1997, pp. 527-532.
53. W.T. Freeman, E.C. Pasztor, Learning low-level vision, in: Proc. IEEE Int. Conf. Comput. Vis., 1999, pp. 1182-1189.
54. J. Besag Spatial interaction and the statistical analysis of lattice systems (with discussion) J. Roy. Stat. Soc. B 36 2 1974 192 326
55. D. Greig, B. Porteous, and A. Seheult Exact maximum a posteriori estimation for binary images J. Roy. Stat. Soc. B 51 2 1989 271 279
56. S. Roy, I.J. Cox, A maximum-flow formulation of the n-camera stereo correspondence problem, in: Proc. IEEE Int. Conf. Comput. Vis., 1998, pp. 492-499.
57. A.V. Goldberg, and R.E. Tarjan A new approach to the maximum flow problem J. ACM 35 1988 921 940
58. Y. Weiss, and W.T. Freeman On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs IEEE Trans. Inf. Theor. 47 2 2001 723 735
59. R.G. Cowell, A.P. Dawid, S.L. Lauritzen, and D.J. Spiegelhalter Probabilistic Networks and Expert Systems 1999 Springer Verlag
60. S. Benedetto, G. Montorsi, D. Divsalar, F. Pollara, Soft-output decoding algorithms in iterative decoding of turbo codes, in: JPL TDA Prog. Rep., vol. 42-124, 1995, pp. 63-87.
61. P.F. Felzenszwalb, and D.P. Huttenlocher Efficient belief propagation for early vis. Int. J. Comput. Vis. 70 1 2006 41 54
62. M. Björkman, D. Kragic, Active 3d segmentation through fixation of previously unseen objects, in: Proc. British Mach. Vis. Conf., 2010, pp. 119.1-119.11.
63. M. Björkman, and J.-O. Eklundh Vision in the real world: finding, attending and recognizing objects Int. J. Imaging Syst. Technol. 16 5 2006 189 209
64. D. Comaniciu, and P. Meer Mean shift: a robust approach toward feature space analysis IEEE Trans. Pattern Anal. Mach. Intell. 24 5 2002 603 619
65. D. Lowe, Object recognition from local scale-invariant features, in: Proc. IEEE Int. Conf. Comput. Vis., vol. 2, 1999, pp. 1150-1157.
66. A.V. Goldberg, R.E. Tarjan, A new approach to the maximum flow problem, in: Proc. ACM Symp. Theory Comput., 1986, pp. 136-146.
67. V.W. Lee, C. Kim, J. Chhugani, M. Deisher, D. Kim, A.D. Nguyen, N. Satish, M. Smelyanski, S. Chennupaty, P. Hammarlund, R. Singhal, and P. Dubey Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU SIGARCH Comput. Archit. News 38 3 2010 451 460
68. V. Vineet, P. Narayanan, CUDA cuts: fast graph cuts on the GPU, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, 2008, pp. 1-8.
69. A. Brunton, C. Shu, G. Roth, Belief propagation on the GPU for stereo vision, in: Proc. Can. Conf. Comput. Robot Vis., 2006, pp. 76-81.
70. Q. Yang, L. Wang, R. Yang, S. Wang, M. Liao, D. Nister, Real-time global stereo matching using hierarchical belief propagation, in: Proc. Br. Mach. Vis. Conf., 2006, pp. 989-998.
71. Y. Xu, H. Chen, R. Klette, J. Liu, and T. Vaudrey Belief propagation implementation using CUDA on an NVidia GTX 280 Adv. Artif. Intell., Lect. Notes Comput. Sci. 5866 2009 180 189
72. Q. Yang, L. Wang, N. Ahuja, A constant-space belief propagation algorithm for stereo matching, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1458-1465.
73. C.-K. Liang, C.-C. Cheng, Y.-C. Lai, L.-G. Chen, and H.H. Chen Hardware-efficient belief propagation IEEE Trans. Circ. Syst. Video Technol. 21 5 2011 525 537
74. A. Vedaldi, B. Fulkerson, VLFeat: an open and portable library of computer vision algorithms, in: Proc. Int. Conf. Multimed., 2010, pp. 1469-1472.
75. N. Bergström, M. Björkman, D. Kragic, Generating object hypotheses in natural scenes through human-robot interaction, in: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2011, pp. 827-833.
76. M. Björkman, Y. Bekiroglu, V. Högman, D. Kragic, Enhancing visual perception of shape through tactile glances, in: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2013, pp. 3180-3186.
77. V. Högman, M. Björkman, D. Kragic, Interactive object classification using sensorimotor contingencies, in: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2013, pp. 2799-2805.