Abstract
We propose a locally regularized snake based on smoothing-spline filtering. The proposed algorithm associates a regularization process with a force equilibrium scheme leading the snake's deformation. In this algorithm, the regularization is implemented with a smoothing of the deformation forces. The regularization level is controlled through a unique parameter that can vary along the contour. It provides a locally regularized smoothing B-snake that offers a powerful framework to introduce prior knowledge. We illustrate the snake behavior on synthetic and real images, with global and local regularization.
Highlights
Active contour models are well adapted for edge detection and segmentation
Xu and Prince [4] defined another external force called gradient vector flow (GVF) that brings a better control on the deformation directions: they proposed to diffuse the gradient over the image according to optical flow theory
Where A is a pentadiagonal banded matrix built from α(k) and β(k) function values, where vectors x and y contain the point coordinates of the discrete version g(k) of the curve g(s), and where vectors fx and fy constitute the external forces computed at the kth point of the snake as follows: f (k) =
Summary
Active contour models (or snakes) are well adapted for edge detection and segmentation. Xu and Prince [4] defined another external force called gradient vector flow (GVF) that brings a better control on the deformation directions: they proposed to diffuse the gradient over the image according to optical flow theory Beside these works, the multiresolution frameworks were integrated within the active contours. Precioso et al [8] proposed a region-based active contour that achieves real-time computation adapted to video segmentation. They extended their model by applying a smoothing B-spline filter [9, 10] on the contour.
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