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T. Chan, L. Vese (2002)
Active Contour and Segmentation Models using Geometric PDE’s for Medical Imaging
Yuhui Shi, R. Eberhart (1998)
Parameter Selection in Particle Swarm Optimization
Donna Williams, M. Shah (1992)
A Fast algorithm for active contours and curvature estimationCVGIP Image Underst., 55
Mahamed Omran, A. Salman, A. Engelbrecht (2006)
Dynamic clustering using particle swarm optimization with application in image segmentationPattern Analysis and Applications, 8
Chenyang Xu, A. Yezzi, J. Prince (2000)
On the relationship between parametric and geometric active contoursConference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154), 1
T. Lindeberg (1994)
Scale-Space Theory : A Basic Tool for Analysing Structures at Different ScalesJournal of Applied Statistics, 21
X. Bresson, S. Esedoglu, P. Vandergheynst, J. Thiran, S. Osher (2007)
Fast Global Minimization of the Active Contour/Snake ModelJournal of Mathematical Imaging and Vision, 28
T. Krink, Morten Løvbjerg (2002)
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
Pablo Arbeláez, L. Cohen (2004)
03 - Segmentation d'images couleur par partitions de VoronoïTraitement Du Signal, 21
(2002)
Chasing the swarm: a predator prey approach to function optimisation
C. Lodato, S. Lopes (2007)
An Optical Flow Based Segmentation Method for Objects ExtractionWorld Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1
James Davis, V. Sharma (2007)
Background-subtraction using contour-based fusion of thermal and visible imageryComput. Vis. Image Underst., 106
L. Ballerini (1999)
Genetic Snakes for Medical Images Segmentation
Arlindo Silva, Ana Neves, E. Costa (2003)
SAPPO: A Simple, Adaptable, Predator Prey Optimiser
Jacob Robinson, S. Sinton, Yahya Rahmat-Samii (2002)
Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antennaIEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313), 1
Yuhui Shi, R. Eberhart (1999)
Empirical study of particle swarm optimizationProceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 3
Jordi Vitrià, P. Radeva (2001)
Region-based approach for discriminant snakesProceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2
J. Kennedy (1997)
The particle swarm: social adaptation of knowledgeProceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)
L. Ballerini (1998)
Genetic snakes for medical image segmentation, 3457
Chenyang Xu, Jerry Prince (1997)
Gradient vector flow: a new external force for snakesProceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
L. Cohen, I. Cohen (1993)
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D ImagesIEEE Trans. Pattern Anal. Mach. Intell., 15
R. Deriche (1987)
Using Canny's criteria to derive a recursively implemented optimal edge detectorInternational Journal of Computer Vision, 1
M. Reddy, D. Kumar (2007)
Multi‐objective particle swarm optimization for generating optimal trade‐offs in reservoir operationHydrological Processes, 21
R. Poli, J. Kennedy, T. Blackwell (1995)
Particle swarm optimizationSwarm Intelligence, 1
M. Kass, A. Witkin, Demetri Terzopoulos (2004)
Snakes: Active contour modelsInternational Journal of Computer Vision, 1
Sun Zheng (2010)
An intensive restraint topology adaptive snake model and its application in tracking dynamic image sequenceInf. Sci., 180
Bertrand Leroy, I. Herlin, L. Cohen (1996)
Multi-resolution algorithms for active contour models, 219
J. Pugh, A. Martinoli, Yizhen Zhang (2005)
Particle swarm optimization for unsupervised robotic learningProceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.
S. Gunn, M. Nixon (1995)
Improving snake performance via a dual active contour
K. Mun, H. Kang, Hwa-Seok Lee, Yoo-Sool Yoon, Chang-Moon Lee, Juneho Park (2004)
Active Contour Model Based Object Contour Detection Using Genetic Algorithm with Wavelet Based Image PreprocessingInternational Journal of Control Automation and Systems, 2
M. Selsis, C. Vieren, F. Cabestaing (1995)
Automatic tracking and 3D localization of moving objects by active contour modelsProceedings of the Intelligent Vehicles '95. Symposium
Xiao-Feng Xie, Wenjun Zhang (2004)
Solving Engineering Design Problems by Social Cognitive Optimization
Jun Shen, S. Castan (1992)
An optimal linear operator for step edge detectionCVGIP Graph. Model. Image Process., 54
P. Arbelaez, L. Cohen
Segmentation d'Images Couleur par Partitions de Voronoi. Revue Traitement du Signal
L. Ballerini (2001)
Genetic Snakes for Color Images Segmentation
V. Caselles, F. Catté, Tomeu Coll, F. Dibos (1993)
A geometric model for active contours in image processingNumerische Mathematik, 66
J. Freixenet, X. Muñoz, D. Raba, J. Martí, X. Cufí (2002)
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
S. Lefèvre, C. Fluck, Benjamin Maillard, N. Vincent (2000)
A Fast Snake-Based Method to Track Football Player
A. Amini, S. Tehrani, T. Weymouth (1988)
Using Dynamic Programming For Minimizing The Energy Of Active Contours In The Presence Of Hard Constraints[1988 Proceedings] Second International Conference on Computer Vision
Z. Cui, J. Zeng, Guoji Sun (2006)
A FAST PARTICLE SWARM OPTIMIZATION
C. Zimmer, J. Olivo-Marin (2005)
Coupled parametric active contoursIEEE Transactions on Pattern Analysis and Machine Intelligence, 27
S. Tisue, U. Wilensky
Netlogo: a simple environment for modeling complexity
F. Lecellier, S. Jehan-Besson, M. Fadili, G. Aubert, M. Revenu, E. Saloux (2006)
Region-Based Active Contour with Noise and Shape Priors2006 International Conference on Image Processing
Chenyang Xu, A. Yezzi, Jerry Prince (2001)
A summary of geometric level-set analogues for a general class of parametric active contour and surface modelsProceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision
Purpose – The purpose of this paper is to describe a work that aims to solve contour detection problem using a planar deformable model and a swarm‐based optimization technique. Contour detection is an important task in image processing as it allows depicting boundaries of objects in an image. The proposed approach uses snakes as active contour model and adapts predator prey optimization (PPO) metaheuristic so that to define a new dynamic for evolving snakes in a way to reduce time complexity while providing good quality results. Design/methodology/approach – In the proposed approach, contour detection has been cast as an optimization problem requiring function minimization. PPO has been used to develop a search strategy to handle the optimization process. PPO is a population‐based method inspired by the phenomenon of predators attack and preys evasion. It has been proposed as an improvement of particle swarm optimization (PSO) where additional particles are introduced to repel the other particles into the swarm. The introduced dynamic is intended to achieve better exploration of the search space. In the design, a representation scheme has been first defined. Each particle either a predator or a prey is represented as a curve (snake) defined by a set of control points. The idea is then to evolve a set of curves using the dynamic governed by PPO model equations. As a result, the curve that optimizes a defined energy function is identified as the contour of the target object. Findings – Application of the proposed method to a variety of images using a multi agent platform has shown that good quality results have been obtained compared to a PSO‐based method. Originality/value – Nature inspired computing is an emergent paradigm that witnesses a growing interest because it suggests a new philosophy to optimization. This work contributes in showing its suitability to solve problems even it is still at infancy. In another hand, despite the amount of work done in image processing, it is still required to define new methods for image segmentation. This work outlines a new way to deal with this problem through the use of PPO.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Jun 5, 2009
Keywords: Optimization; Image processing
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