On active contour models and balloons
CVGIP: Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
GA-PSO based vector control of indirect three phase induction motor
Applied Soft Computing
A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
A hybrid watermarking technique applied to digital images
Applied Soft Computing
Expert Systems with Applications: An International Journal
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Active contour model with gradient directional information: directional snake
IEEE Transactions on Circuits and Systems for Video Technology
Multi-swarm particle swarm optimization based risk management model for virtual enterprise
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Expert Systems with Applications: An International Journal
Parametric active contour model by using the honey bee mating optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach
Expert Systems with Applications: An International Journal
Particle Swarm Optimization and Differential Evolution for model-based object detection
Applied Soft Computing
Credit portfolio management using two-level particle swarm optimization
Information Sciences: an International Journal
ORACM: Online region-based active contour model
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
In this paper, the multi-population particle swarm optimization (PSO) is utilized to enhance the concavity searching capability for the control points of active contour model (ACM). In the traditional methods for ACM, each control point searches its new position in a small nearby window. Consequently, the boundary concavities cannot be searched accurately. Some improvements have been made in the past to enlarge the searching space, yet they are still time-consuming. To overcome these drawbacks, a multi-population particle swarm optimization technique is adopted in this paper to reduce the search time but in a larger searching window. In the proposed scheme, to each control point in the contour there is a corresponding swarm of particles with the best swarm particle as the new control point. The proposed optimizer not only inherits the spirit of the original PSO in each swarm but also shares information of the surrounding swarms. Experimental results demonstrate that the proposed method can improve the search of object concavities without extra computation time.