Predator prey optimization for snake-based contour detection

International Journal of Intelligent Computing and Cybernetics

Volumn 2, Issue 2, 2009, Pages 228-242

  Nebti, Salima ^a   Meshoul, Souham ^b  

^a Universite Mentouri Constantine   (Algeria)

^b KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

Image processing; Optimization

Indexed keywords

ACTIVE CONTOUR MODEL; CONTOUR DETECTION; CONTROL POINT; DEFORMABLE MODELS; DESIGN/METHODOLOGY/APPROACH; ENERGY FUNCTIONS; METAHEURISTIC; MODEL EQUATIONS; MULTI-AGENT PLATFORMS; OPTIMIZATION PROBLEMS; OPTIMIZATION PROCESS; OPTIMIZATION TECHNIQUES; PREDATOR-PREY; REPRESENTATION SCHEMES; SEARCH SPACES; SEARCH STRATEGIES; TARGET OBJECT; TIME COMPLEXITY;

CURVE FITTING; ENZYMES; GRAPH THEORY; IMAGE PROCESSING; IMAGING SYSTEMS; OBJECT RECOGNITION; PHILOSOPHICAL ASPECTS; PROBLEM SOLVING; SET THEORY;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 78049237489     PISSN: 1756378X     EISSN: 17563798     Source Type: Journal    
DOI: 10.1108/17563780910959884     Document Type: Article

References (43)

1. Amini, A., Tehrani, S. and Weymouth, T. (1988), Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints, Proceedings of the International Conference on Computer Vision, Tampa, FL, pp. 95-9.
2. Arbelaez, P. and Cohen, L. (2004), Segmentation d'Images Couleur par Partitions de Voronoi. Revue Traitement du Signal, Numero Special Image Couleur, Vol. 21, No.5, pp. 407-21.
3. Ballerini, L. (1999), Genetic snakes for medical images segmentation, Lectures Notes in Computer Science, Vol. 1596, pp. 59-73, Springer, Berlin.
4. Ballerini, L. (2001), Genetic snakes for medical images segmentation, Lectures Notes in Computer Science, Application of Evolutionary Computation, Vol. 2037, pp. 268-77, Springer, Berlin.
5. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.P. and Osher, S. (2007), Fast global minimization of the active contour/snake model, Journal of Mathematical Imaging and Vision, Vol. 28, No.2, pp. 151-67.
6. Caselles, V., Catte, F., Coll, T. and Dibos, F. (1993), A geometric model for active contours in image processing, Numerische Mathematik, Vol. 66, No.1, pp. 1-31.
7. Cohen, L.D. (1993), Finite-element methods for active contour models and balloons for 2-D and 3-D images, PAMI, Vol. 15, No.11, pp. 1131-47.
8. Cui, Z.H., Zeng, J.C. and Sun, G.J. (2006), A fast particle swarm optimization, International Journal of Innovative Computing, Information and Control (ICIC), Vol. 6, pp. 1365-80.
9. Davis, J.W. and Sharma, V. (2007), Background-subtraction using contour-based fusion of thermal and visible imagery, Computer Vision and Image Understanding, Vol. 106, pp. 162-82.
10. Deriche, R. (1987), Using Canny's criteria to derive a recursively implemented optimal edge detector, International Journal of Computer Vision, Vol. 1, pp. 167-87.
11. Freixenet, J., Muñoz, X., Raba, D., Marti, J. and Cufi, X. (2002), Yet another survey on image segmentation: region and boundary information integration, Proceedings of the European Conference on Computer Vision (ECCV '02), Copenhagen, Denmark, pp. 408-22.
12. Gunn, S.R. and Nixon, M.S. (1995), Improving snake performance via a dual active contour, Hlavac, V., Sara, R. (Eds.), Proceedings of the Computer Analysis of Images and Patterns 970, Prague, Czech Republic, pp. 600-5.
13. Kass, M., Witkin, A. and Terzopoulos, D. (1987), Snakes: active contour models, International Journal of Computer Vision, Vol. 1, No.4, pp. 321-33.
14. Kennedy, J. and Eberhart, R.C. (1995), Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, Vol. 4, pp. 1942-8.
15. Krink, T. and Løvbjerg, M. (2002), The life cycle model: combining particle swarm optimisation, genetic algorithms and hillClimbers, Lecture Notes in Computer Science (LNCS) No. 2439, Proceedings of Parallel Problem Solving from Nature VII (PPSN), pp. 621-30.
16. Kyeong, J.M., Tae Kang, H., Lee, H.S., Yoon, Y.S., Lee, C.M. and Ho Park, J. (2004), Active contour model based object contour detection using genetic algorithm with wavelet based image preprocessing, International Journal of Control, Automation, and Systems, Vol. 2, No.1, pp. 100-6.
17. Lecelliera, F., Bessona, J., Fadilia, S.J., Aubertb, G., Revenua, M. and Salouxc, E. (2006), Region-based active contour with noise and shape priors, International Conference on Image Processing (ICIP06), New York, NY, pp. 1649-52.
18. Leroy, B., Herlin, I. and Cohen, L. (1996), Multi-resolution algorithms for active contour models, 12th International Conference on Analysis and Optimization of Systems, Paris, pp. 58-65.
19. Lindeberg, T. (1994), Scale-space theory: a basic tool for analysing structures at different scales, Journal of Applied Statistics, Vol. 21, No.2, pp. 224-70.
20. Lodato, C. and Lopes, S. (2006), An optical flow based segmentation method for objects extraction, Transactions on Engineering, Computing and Technology, Vol. 12.
21. Omran, G.M., Salman, A.A. and Engelbrecht, A.P. (2006), Dynamic clustering using particle swarm optimization with application in image segmentation, Pattern Anal. Appl., Vol. 8, No.4, pp. 332-44.
22. Pugh, J., Zhang, Y. and Martinoli, A. (2005), Particle swarm optimization for unsupervised robotic learning, Swarm Intelligence Symposium, Pasadena, CA, pp. 92-9.
23. Radeva, P. and Vitria, J. (2001), Region-based approach for discriminant snakes, Proceedings of the International Conference on Image Processing ICIP01, Thessaloniki, Greece, pp. 801-4.
24. Reddy, J.M. and Kumar, D.N. (2007), Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation, Hydrol Process, Vol. 21, No.21, pp. 2897-909.
25. Robinson, J., Sinton, S. and Rahmat-Samii, Y. (2002), Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna, paper presented at IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, San Antonio, TX.
26. Sébastien, L., Cyril, F., Benjamin, M. and Nicole, V. (2000), A fast snake-based method to track football players, IAPR International Workshop on Machine Vision Applications, Tokyo, pp. 501-4.
27. Shen, J. and Castan, S. (1991), An optimal linear operator for step edge detection, Graphical Models and Image Processing CVGIP, Vol. 54, No.2, pp. 112-33.
28. Shi, Y. and Eberhart, R. (1999), Empirical study of particle swarm optimization, Proceedings of the Congress on Evolutionary Computation, Piscataway, NJ, pp. 1945-50.
29. Silva, A., Neves, A. and Costa, E. (2002), Chasing the swarm: a predator prey approach to function optimisation, paper presented at 8th International Conference on Soft Computing, Brno.
30. Tisue, S. and Wilensky, U. (2004), Netlogo: a simple environment for modeling complexity, International Conference on Complex Systems, Boston, MA, pp. 141-50.
31. Williams, D.J. and Shah, M. (1992), A fast algorithm for active contours and curvature estimation, CVIGP Computer Vision Graphics Image Process: Image Understanding, Vol. 55, No.1, pp. 14-26.
32. Xu, C. and Prince, J.L. (1997), Gradient vector flow: a new external force for snakes, IEEE Conference on Computer Vision and Pattern Recognition. (CVPR'97) Los Alamitos, CA, pp. 66-71.
33. Xu, C., Yezzi, A. Jr and Prince, J.L. (2000), On the relationship between parametric and geometric active contours, Proceedings of 34th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 483-9.
34. Xu, C., Yezzi, A. Jr and Prince, J.L. (2001), A summary of geometric level-set analogues for a general class of parametric active contour and surface models, Proceedings of the IEEE Workshop on Variational and Level Set Methods in Comp. Vis, Vancouver, Canada, pp. 104-11.
35. Zimmer, C., Christophe, J. and Marin, O. (2005), Coupled parametric active contours, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No.11, pp. 1838-42.
36. Ballerini, L. (1998), Genetic snakes for medical images segmentation, Mathematical Modeling and Estimation Techniques in Computer Vision, SPIE, Vol. 3457, pp. 284-95.
37. Chan, T. and Vese, L. (2002), Active contour and segmentation models using geometric PDE's for medical imaging, Malladi, R. (Ed.), Geometric Methods in Bio-Medical Image Processing, pp. 63-75, Springer, Berlin.
38. Kass, M. (1988), Snakes: active contour models, International Journal of Computer Vision, Vol. 1, pp. 321-31.
39. Kennedy, J. (1997), The particle swarm: social adaptation of knowledge, IEEE International Conference on Evolutionary Computation, Indianapolis, IN, pp. 303-8.
40. Selsis, M., Vieren, C. and Cabestaing, F. (1995), Automatic tracking and 3D localization of moving objects by active contour model, Proceedings of the IEEE Intelligent Vehicles Symposium, Detroit, MI, USA, pp. 96-100.
41. Shi, Y. and Eberhart, R.C. (1998), Parameter selection in particle swarm optimization, Evolutionary Programming VII: Proceedings of the EP98, Springer, New York, NY, pp. 591-600.
42. Silva, A., Neves, A. and Costa, E. (2003), SAPPO: a simple, adaptive, predator prey optimiser, Proceedings of the 11th Portuguese Conference on Artificial Intelligence, Workshop on Artificial Life and Evolutionary Algorithms (ALEA), EPIA'03, Beja, Portugal, pp. 59-73.
43. Xiao-Feng, X. and Zhang, W.J. (2004), Solving engineering design problems by social cognitive optimization, Genetic and Evolutionary Computation Conference (GECCO), Seattle, WA, LNCS 3102, pp. 261-2.