Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Graphical Gaussian Shape Models and Their Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
Boundary Finding with Correspondence Using Statistical Shape Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
An Integrated Approach for Surface Finding in Medical Images
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Quadric-based simplification in any dimension
ACM Transactions on Graphics (TOG)
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
A fast minimal path active contour model
IEEE Transactions on Image Processing
Pattern Recognition Letters
Hi-index | 0.01 |
In this paper, an improved active contour model based on the time-adaptive self-organizing map with a high convergence speed and low computational complexity is proposed. For this purpose, the active contour model based on the original time-adaptive self-organizing map is modified in two ways: adaptation of the speed parameter and reduction of the number of neurons. By adapting the speed parameter, the neuron motion speed is determined based on the distance of each neuron from the shape boundary which results in an increase in the speed of convergence of the contour. Using a smaller number of neurons, the computational complexity is reduced. To achieve this, the number of neurons used in the contour is determined based on the boundary curvature. The proposed model is studied and compared with the original time-adaptive self-organizing map. Both models are used in several experiments including a tracking application. Results reveal the higher speed and very good performance of the proposed model for real-time applications.