Fast probabilistic snake algorithm

TL;DR

This article is presenting an active contour algorithm based on a probability approach inspired by A. Blake work and P. Refregier's team research in France, which is both very fast and highly accurate as far as contour description is concerned.

Abstract

Few people use the probability theory in order to achieve image segmentation with snake models. In this article, we are presenting an active contour algorithm based on a probability approach inspired by A. Blake work and P. R{é}fr{é}gier's team research in France. Our algorithm, both very fast and highly accurate as far as contour description is concerned, is easily adaptable to any specific application.

Figures (5)

• Figure 1: Ideal edge image and variance along the normal of $N_i$
• Figure 2: Regularization: (a) $p(N_i|I)$, (b) the regularity function, (c) $p(N_i|I)$ regularized.
• Figure 3: Results in the closed curve case obtain. Initialization (a), classical snake (b), CASP model (c) and our statistical model (d)
• Figure 4: Exemple of tank segmentation
• Figure 5: Results in the open curve case obtain. Initialization (a), classical snake (b), our statistical model (c)