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Genetic Algorithms for motion estimation
Sophie Voisin Imaging, Robotics, & Intelligent Systems Laboratory The University of Tennessee October 12, 2004
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Outlines Le2i laboratory My previous work My future work at IRIS
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Le2i Le2i : www.le2i.com 2 sites : Dijon and Le Creusot
Laboratoire Électronique Informatique et Image 2 sites : Dijon and Le Creusot September 21, Slide 3
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Le2i Le2i : www.le2i.com 3 teams - 3 different ways :
Laboratoire Électronique Informatique et Image 3 teams - 3 different ways : Electronics, Architecture and Material Integration Dijon, Le Creusot Material Signal Processing and Image Processing Processing Computer Science and Image Dijon Theory September 21, Slide 4
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My previous works DEA* research 4 months (full time) Advisor :
Doctor Albert Dipanda** Application of Genetic Algorithms for motion estimation based on Markov random fields : The aim of this research was to find how we can use genetic algorithms for motion estimation, to implement this algorithm and to compare it to different existed ones. *Master Degree * *Processing team, Dijon September 21, Slide 5
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DEA research Assumption Optical flow
In image sequence the color for one point of object is the same all over the time no estimation is possible estimation is possible September 21, Slide 6
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DEA research Genetic Algorithms The theory Principle
Genetic algorithms are inspired by Darwin's theory of evolution They were applied for the first time by John Holland 1975 Initial population Estimation (fitness) Reproduction Reproduction / Elitism Selection Next population / Offspring Crossover Estimation (fitness) Mutation Stop criterion no yes Solution September 21, Slide 7
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DEA research Genetic Algorithms The theory Encoding of a Chromosome
Representation of possible solution Fitness To estimate an individual in a population Operators for reproduction Selection Crossover Mutation September 21, Slide 8
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DEA research Genetic Algorithms How use them for motion estimation
Encoding of a Chromosome Values Vectors with amplitude equals to 0 or 1 (approximately) 2-D structure in general 1-D structure is used (1,-1) (0,-1) (-1,-1) (1,0) (0,0) (-1,0) (1,1) (0,1) (-1,1) Pixel (i, j) Pixel (i+1, j+1) Image Result Image 1 Image 2 September 21, Slide 9
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DEA research Genetic Algorithms How use them for motion estimation
Fitness Function of energy based on optical flow and Markov random fields *Displaced frame difference **Regularization Tikhonov or Mc Clure September 21, Slide 10
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DEA research Genetic Algorithms How use them for motion estimation
Operators Selection Tournament September 21, Slide 11
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DEA research Genetic Algorithms 1 How use them for motion estimation
Operators Crossover Chromosomes 2 points Linear combination Parents Children Children 1 1 Key 2 September 21, Slide 12
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Mutation by small variation
DEA research Genetic Algorithms How use them for motion estimation Operators Mutations Individual Random mutation Mutation by small variation Directed mutation ? September 21, Slide 13
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DEA research First Tests Key Zoom x1 x4 x16 x64 Height x Width 64 x 64
Individuals per population 501 151 75 Number of generation 5,000 2,500 10,000 1,000 Explored individuals (possible solutions) 2,505,000 377,000 1,510,000 75,000 Time 2.4 hours 5.5 minutes 6.4 minutes 44.4 seconds September 21, Slide 14
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DEA research Improvement Results To Split images in “sub-images”
First cut in blue Second cut in red Results 10 minutes 64x64 images Non homogenous background Key First cut Second cut Result September 21, Slide 15
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Results and Conclusions
Key Results and Conclusions Initialization Method of Gradient Iterative Conditional Mode Simulated Annealing Step Genetic Algorithms Random blocks of motion Time: 1.9 s Time: 1.5 s Time: 5.7 s Random motion for each point First Time: 5 min Null motion for each point Time: 2.5 s Time: 1.8 s Time: 6.2 s Second Time: 4 min Time: 2.1 s Time: 2 s Time: 7.3 s Final Time: 9 min Deterministic algorithms : initialization dependence Simulated Annealing : chaotic motion for homogenous background Genetic Algorithms : no real time circle rotation square translation September 21, Slide 16
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DEA research Results : “train sequence” 400 x 512 image
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DEA research Results : “train sequence” 400 x 512 image
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DEA research Results : “train sequence” 400 x 512 image
Method of Simulated Gradient Annealing 46 s min Genetic ICM Algorithms 19 s h30 min Key September 21, Slide 19
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DEA research The Most Important References
S . Woo - Reconstruction de Surface 3D et Estimation du Mouvement Rigide 3D à Partir d’un Système de Stéréovision Active (Application des Algorithmes Génétiques à l’ Analyse d’images). Thèse soutenue en 2003. P. Perez - Champs de Markoviens et Analyse Multirésolution de l’ Image : Application à l’ Analyse de Mouvement. Thèse soutenue en 1993. A. Dipanda, L. Legrand - Multiresolution Motion Estimation with Discontinuities Preservation Using MRF and Determination of the Regularization Hyperparameter. SDII, 1999, San José. A. Dipanda, S. Woo, F. Marzani, J-M. Bilbault - 3-D Shape Reconstruction in an Active Stereo Vision System Using Genetic Algorithms. Pattern Recognition 36 (2003) M. Zaim, A. El ouaazizi, R. Benslimane - Genetic Algorithms Based Motion Estimation L. Li, S. J. Louis, J. N Brune - Application of Genetic Algorithms to 2-D Velocity of Seismic Refraction Data. In Proceedings of the Third Golden West International Conference on Intelligent Systems. Kluwer Academic Press. (to appear 1995). September 21, Slide 20
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My future work at IRIS To have my PhD Thesis French advisors :
Professor Frederic Truchetet Doctor Sebti Foufou September 21, Slide 21
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3-D Reconstruction and Segmentation
My future work at IRIS 3-D Reconstruction and Segmentation Task 1 Topic Applications Task 2 Sate of the Art Contribution Task 3 Research Implementation Task 4 Results / Conclusion Discussions / Future Research September 21, Slide 22
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Questions ? Thanks September 21, Slide 23
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