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
Algorithms for Image Processing and Computer Vision
Algorithms for Image Processing and Computer Vision
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
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
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Arranging and interpolating sparse unorganized feature points with geodesic circular arc
IEEE Transactions on Image Processing
Coarse-to-fine boundary location with a SOM-like method
IEEE Transactions on Neural Networks
Binary tree time adaptive self-organizing map
Neurocomputing
Pattern Recognition Letters
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Active contour modeling is a powerful technique for modeling object boundaries. Various methods introduced for this purpose, however, have certain difficulties such as getting stuck in local minima, poor modeling of long concavities, and producing inaccurate results when the initial contour is chosen simple or far from the object boundary. A modified form of time adaptive self-organizing map network with a variable number of neurons is proposed here for active contour modeling which does not show such difficulties and automatically determines the required number of control points. The initial contour for the object boundary can be defined inside, outside, or across the boundary. This contour can be open or closed, may be as simple as desired, and can be placed far from the object boundary. In addition, the boundary may contain long concavities. The proposed algorithm is tested for modeling different objects and shows very good performance.