Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | 13 | 6 | 3092-3105
Tytuł artykułu

Particle Swarm Optimization and Differential Evolution for model-based object detection

Treść / Zawartość
Abstrakt, słowa kluczowe
Źródło
Twórcy
Bibliografia
Dodatkowe informacje
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition.The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation.In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subject's posture in space.Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIA™ CUDA computing architecture.
Czasopismo
Rocznik
Tom
13
Numer
6
Strony
3092-3105
Opis fizyczny
Twórcy
  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy , rob_ugo@ce.unipr.it
  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy , nashed@ce.unipr.it
autor
  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy , pmesejo@ce.unipr.it
autor
  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy , mussi@ce.unipr.it
  • Henesis s.r.l., Viale dei Mille 108, 43125 Parma, Italy
  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy , cagnoni@ce.unipr.it
Bibliografia
  • 1. Floudas, C.& Gounaris, C., "A review of recent advances in global optimization", Journal of Global Optimization, vol. 45, 2009, p.3-38
  • 2. Zhigljavsky, A.& Zilinskas, A., "Stochastic Global Optimization", 2007
  • 3. Eiben, A.E.& Smith, J.E., "Introduction to Evolutionary Computing", 2003
  • 4. Engelbrecht, A.P., "Computational Intelligence: An Introduction", 2007, 2nd ed.
  • 5. Ugolotti, R.& Mesejo, P.& Cagnoni, S.& Giacobini, M.& Di Cunto, F., "Automatic hippocampus localization in histological images using PSO-based deformable models", Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’11 (Companion), 2011
  • 6. L. Mussi, Š. Ivekovič, S. Cagnoni, Markerless articulated human body tracking from multi-view video with GPU-PSO, in: G. Tempesti, A.M. Tyrrell, J.F. Miller (Eds.), Evolvable Systems: From Biology to Hardware – 9th International Conference, ICES 2010, York, UK, September 2010 Proceedings, 2010, pp. 195–207.
  • 7. nVIDIA, nVIDIA CUDA Programming Guide v. 4.0, nVIDIA Corporation, 2011.
  • 8. Terzopoulos, D.& Fleischer, K., "Deformable models", The Visual Computer, vol. 4, 1988, p.306-331
  • 9. Terzopoulos, D.& Witkin, A.& Kass, M., "Constraints on deformable models: recovering 3D Shape and nonrigid motion", Artificial Intelligence, vol. 36, 1988, p.91-123
  • 10. Kass, M.& Witkin, A.& Terzopoulos, D., "Snakes: active contour models", International Journal of Computer Vision, vol. 1, 1988, p.321-331
  • 11. Cootes, T.F.& Taylor, C.J.& Cooper, D.H.& Graham, J., "Active shape models – their training and application", Computer Vision and Image Understanding, vol. 61, 1995, p.38-59
  • 12. Cootes, T.F.& Edwards, G.J.& Taylor, C.J., "Active appearance models", Proceedings of the European Conference on Computer Vision, vol. 2, 1998, p.484-498
  • 13. Zhong, Y.& Jain, A.K., "Object tracking using deformable templates", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, 2000, p.544-549
  • 14. Jain, A.K.& Zhong, Y.& Lakshmanan, S., "Object matching using deformable templates", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, 1996, p.267-278
  • 15. J. Kennedy, R. Eberhart, Particle Swarm Optimization, in: Proceedings of IEEE International Conference on Neural Networks, ICNN’95, vol. 4, 1995, pp. 1942–1948.
  • 16. Poli, R., "Analysis of the publications on the applications of Particle Swarm Optimisation", Journal of Artificial Evolution and Applications, 2008, p.1-10
  • 17. Banks, A.& Vincent, J.& Anyakoha, C., "A review of Particle Swarm Optimization. Part I. Background and development", Natural Computing, vol. 6, 2007, p.467-484
  • 18. Mussi, L.& Daolio, F.& Cagnoni, S., "Evaluation of parallel Particle Swarm Optimization algorithms within the CUDA architecture", Information Sciences, vol. 181, 2011, p.4642-4657
  • 19. Mussi, L.& Nashed, Y.S.& Cagnoni, S., "GPU-based asynchronous Particle Swarm Optimization", Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO’11, 2011, p.1555-1562
  • 20. R. Storn, K. Price, Differential Evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report, International Computer Science Institute, 1995.
  • 21. Vesterstrom, J.& Thomsen, R., "A comparative study of Differential Evolution, Particle Swarm Optimization, and evolutionary algorithms on numerical benchmark problems", IEEE Congress on Evolutionary Computation, CEC2004, 2004, p.1980-1987
  • 22. Das, S.& Suganthan, P., "Differential Evolution: a survey of the state-of-the-art", IEEE Transactions on Evolutionary Computation, vol. 15, 2011, p.4-31
  • 23. Neri, F.& Tirronen, V., "Recent advances in Differential Evolution: a survey and experimental analysis", Artificial Intelligence Review, vol. 33, 2010, p.61-106
  • 24. N. Hansen, S. Finck, R. Ros, A. Auger, Real-parameter black-box optimization benchmarking 2009: noiseless functions definitions, Research Report, INRIA, 2009.
  • 25. N. Hansen, R. Ros, N. Mauny, M. Schoenauer, A. Auger, PSO facing non-separable and ill-conditioned problems, Research Report RR-6447, INRIA, 2008.
  • 26. Owens, J.D.& Luebke, D.& Govindaraju, N.& Harris, M.& Krger, J.& Lefohn, A.E.& Purcell, T.J., "A survey of general-purpose computation on graphics hardware", Computer Graphics Forum, vol. 26, 2007, p.80-113
  • 27. Hager, G.& Zeiser, T.& Wellein, G., "Data access optimizations for highly threaded multi-core CPUs with multiple memory controllers", IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008, 2008, p.1-7
  • 28. McIntosh, C.& Hamarneh, G., "Evolutionary deformable models for medical image segmentation: a genetic algorithm approach to optimizing learned, intuitive, and localized medial-based shape deformation", Smith, S.& Cagnoni, S. (Eds.), Genetic and Evolutionary Computation: Medical Applications, 2010, p.46-67
  • 29. MacEachern, L.& Manku, T., "Genetic algorithms for active contour optimization", Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS’98, vol. 4, 1998, p.229-232
  • 30. L. Ballerini, Genetic snakes for color images segmentation, in: Applications of Evolutionary Computing, vol. 2037 of Lecture Notes in Computer Science, Springer, 2001, pp. 268–277.
  • 31. Ghosh, P.& Mitchell, M., "Segmentation of medical images using a genetic algorithm", Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO’06, 2006, p.1171-1178
  • 32. Fan, Y.& Jiang, T.& Evans, D., "Volumetric segmentation of brain images using parallel genetic algorithms", IEEE Transactions on Medical Imaging, vol. 21, 2002, p.904-909
  • 33. Cagnoni, S.& Dobrzeniecki, A.B.& Poli, R.& Yanch, J.C., "Genetic algorithm-based interactive segmentation of 3D medical images", Image and Vision Computing, vol. 17, 1999, p.881-895
  • 34. Asl, M.& Seyedin, S., "Active contour optimization using Particle Swarm Optimizer", Information and Communication Technologies, ICTTA’06, vol. 1, 2006, p.1522-1523
  • 35. Tseng, C.-C.& Hsieh, J.-G.& Jeng, J.-H., "Active contour model via multi-population Particle Swarm Optimization", Expert Systems with Applications, vol. 36, 2009, p.5348-5352
  • 36. Novo, J.& Santos, J.& Penedo, M.G., "Optimization of topological active nets with Differential Evolution", Proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA’11, 2011, p.350-360
  • 37. Rymut, B.& Kwolek, B., "GPU-supported object tracking using adaptive appearance models and Particle Swarm Optimization", Proceedings of the 2010 International Conference on Computer Vision and Graphics: Part II, ICCVG’10, 2010, p.227-234
  • 38. T. Krzeszowski, B. Kwolek, K.W. Wojciechowski, GPU-accelerated tracking of the motion of 3D articulated figure, in: L. Bolc, R. Tadeusiewicz, L.J. Chmielewski, K.W. Wojciechowski (Eds.), ICCVG (1), vol. 6374, 2010, pp. 155–162.
  • 39. Ivekovič, Š.& Trucco, E.& Petillot, Y.R., "Human body pose estimation with Particle Swarm Optimisation", Evolution Computing, vol. 16, 2008, p.509-528
  • 40. John, V.& Trucco, E.& Ivekovič, Š., "Markerless human articulated tracking using hierarchical particle swarm optimisation", Image and Vision Computing, vol. 28, 2010, p.1530-1547
  • 41. Bandouch, J.& Engstler, F.& Beetz, M., "Evaluation of hierarchical sampling strategies in 3D human pose estimation", Proceedings of the 19th British Machine Vision Conference (BMVC), 2008
  • 42. Deutscher, J.& Reid, I., "Articulated body motion capture by stochastic search", International Journal of Computer Vision, vol. 61, 2005, p.185-205
  • 43. MacCormick, J.& Isard, M., "Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking", Vernon, D. (Eds.), Proceedings of the 6th European Conference on Computer Vision-Part II (ECCV’00), 2000, p.3-19
  • 44. Caillette, F.& Galata, A.& Howard, T., "Real-time 3-D human body tracking using learnt models of behaviour", Computer Vision and Image Understanding, vol. 109, 2008, p.112-125
  • 45. Urtasun, R.& Fleet, D.J.& Hertzmann, A.& Fua, P., "Priors for people tracking from small training sets", Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05), vol. 1, 2005, p.403-410
  • 46. Gall, J.& Rosenhahn, B.& Brox, T.& Seidel, H.-P., "Optimization and filtering for human motion capture", International Journal of Computer Vision, vol. 87, 2010, p.75-92
  • 47. Warren, J.& Schaefer, S., "A factored approach to subdivision surfaces", Computer Graphics and Applications, vol. 24, 3, 2004, p.74-81
  • 48. Barnes, J.& Bartlett, J.W.& van de Pol, L.A.& Loy, C.T.& Scahill, R.I.& Frost, C.& Thompson, P.& Fox, N.C., "A meta-analysis of hippocampal atrophy rates in Alzheimer's disease", Neurobiology of Aging, vol. 30, 2009, p.1711-1723
  • 49. Terry, R.D.& Davies, P., "Dementia of the Alzheimer type", Annual Review of Neuroscience, vol. 3, 1980, p.77-95
  • 50. Allen Institute for Brain Science, Allen Reference Atlases, http://mouse.brain-map.org, 2004–2006.
  • 51. Wilcoxon, F., "Individual comparisons by ranking methods", Biometrics Bulletin, 1945, p.80-83
  • 52. Wolpert, D.& Macready, W., "No free lunch theorems for optimization", IEEE Transactions on Evolutionary Computation, vol. 1, 1, 1997, p.67-82
  • 53. Caponio, A.& Neri, F.& Tirronen, V., "Super-fit control adaptation in memetic Differential Evolution frameworks", Soft Computing, vol. 13, 8–9, 2009, p.811-831
  • 54. Cagnoni, S.& Cordón, O.& Mesejo, P.& Nashed, Y.S.G.& Ugolotti, R., "First results and future developments of the MIBISOC Project in the IBISlab of the University of Parma", Soule, T. (Eds.), Proceedings of the 14th international conference on Genetic and evolutionary computation conference companion (GECCO Companion’12), 2012, p.509-516
  • 55. Brest, J.& Bošković, B.& Greiner, S.& Žumer, V.& Maučec, M., "Performance comparison of self-adaptive and adaptive Differential Evolution algorithms", Soft Computing: A Fusion of Foundations, Methodologies and Applications, vol. 11, 7, 2007, p.617-629
  • 56. Weber, M.& Tirronen, V.& Neri, F., "Scale factor inheritance mechanism in distributed Differential Evolution", Soft Computing, vol. 14, 2010, p.1187-1207
Kolekcja
Elsevier
Identyfikator YADDA
bwmeta1.element.elsevier-3737f7df-7ae0-3755-895c-17276371d86a
Identyfikatory
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.