An improved time-adaptive self-organizing map for high-speed shape modeling
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.
time-adaptive self-organizing map active contour model boundary curvature tasom adaptive speed parameter person tracking