In this paper, we introduce a new approach to parametric snake method by using variable snake parameters. Adopting fixed parameter values for all points of the snake, as usual, constitutes by itself a limitation that leads to poor performances in terms of convergence and tracking properties. A more adapted choice should be the one that allows selection depending on the image region properties as on the contour shape and position. However, such variability is not an easy task in general and a precise method need to be defined to assure contour point dependent tuning at iterations. We were particularly interested in applying this idea to the recently presented parametric method . In the work mentioned, an attraction term is used to improve the convergence of the standard parametric snake without a significant increase in computational load. We show here, that improved performances can ensue from applying variable parameter concepts. For this purpose, the method is first analyzed and then a procedure is developed to assure an automatic variable parameter tuning. The interest of our approach is illustrated through object segmentation results.
In this paper, we propose an improvement of the classical parametric active contours. The method, presented here, consists in adding a new energy term based on the object center of gravity distance map. This additional term acts as attraction forces that constrain the contour to remain in the vicinity of the object. The distance map introduced here differs from the classical one since it is not based on a binary image, but rather constitutes a simplified and very fast version that relates only to one point, defined as the expected center of gravity of the object. The additional forces, so introduced, act as a kind of balloon method with improved convergence. The method is evaluated for object segmentation in images, and also for object tracking. The center of gravity is computed from the initial contour for each image of the sequence considered. Compared to the balloon method, the presented approach appears to be faster and less prone to loops, as it behaves better for object tracking.