Fast and Regularization less Active Contour

Fast and Regularization less Active Contour

The application of the level set method in image segmentation has been very popular due to its capability of automatically handling changes in topology. However, a re-initialization procedure, which leads to expensive computation, is required in the traditional level set method to keep the level set function as a signed distance function to its interface. A method based on Gaussian filtering an...

Fast Implicit Active Contour Models

Implicit active contour models are widely used in image processing and computer vision tasks. Most implementations, however, are based on explicit updating schemes and are therefore of limited computational efficiency. In this paper, we present fast algorithms based on the semi-implicit additive operator splitting (AOS) scheme for both the geometric and the geodesic active contour model. Our ex...

FACE: fast active-contour curvature-based evolution

This paper presents an active contour model for fast object segmentation called FACE. A novel energy term that takes into account the computational complexity of the active contour is introduced together with related constraints and minimization procedure. The described process is based on the regularization and optimization of the active contour control points position. The trade-off between c...

Fast Methods for Implicit Active Contour Models

A Fast and Efficient Wavelet-Based Active Contour Method

In this paper, we propose a new method for fast and effective image segmentation using snakes (active contours). This method incorporates the active contour models with the wavelet transform features using directional edges in high and low frequency content sub-images. The proposed method takes advantages of these features to improve the boundary attraction process in active contour models. It ...