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
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Evolutionary fronts for topology-independent shape modeling and recovery
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Tracking Deformable Objects in the Plane Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAC: Magnetostatic Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Locating and extracting the eye in human face images
Pattern Recognition
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
IEEE Transactions on Information Technology in Biomedicine
TASOM: a new time adaptive self-organizing map
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Multiple contour extraction from graylevel images using an artificial neural network
IEEE Transactions on Image Processing
Active Contour External Force Using Vector Field Convolution for Image Segmentation
IEEE Transactions on Image Processing
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Active contour models are widely used in extracting object boundaries. However, most of these models usually fail to capture concave boundaries properly and impose high computational cost. In this paper, a new active contour model based on the Conscience, Archiving and Mean-Movement mechanisms and the SOM (CAMSOM) is proposed to eliminate these deficiencies. The proposed method extends the Batch SOM method (BSOM) by introducing three mechanisms of Conscience, Archiving and Mean-Movement mechanisms. To evaluate the performance of the proposed method compared with both energy minimization and SOM-based methods, some experiments are carried out on a set of grayscale images including synthetic and real ones. The experimental results are compared with those of the BSOM in terms of accuracy and convergence speed. The results reveal that, compared to BSOM, the proposed method requires less computations to converge to the object boundaries and extracts the boundaries of complex objects more accurately, even in the presence of weak or broken edges. Moreover, CAMSOM has higher performance and accuracy in capturing the boundaries of the objects placed arbitrarily in a multi-object scene, whereas the performance of BSOM in multi-object scenes highly depends on the arrangement of the objects. Compared to the energy minimization methods, the proposed method can accurately and quickly converges to the concave boundaries, whereas gradient vector flow (GVF) and vector field convolution (VFC) which are two well-known energy minimization methods get stuck in local minima and cannot proceed to the end of the concavity.