Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Extraction from Volumetric Images Using Deformable Meshes: A Comparative Study
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Active contour model via multi-population particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Honey Bee Mating Optimization Scheme for Active Contour Model
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
B-spline snakes: a flexible tool for parametric contour detection
IEEE Transactions on Image Processing
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
An evolutionary image matching approach
Applied Soft Computing
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In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time.