Niels Chr. Overgaard

Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors

In this paper we address the problem of segmentation in image sequences using region-based active contours and level set methods. We propose a novel method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the classical Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It is expected to handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.

Deformable Shape Priors in Chan-Vese Segmentation of Image Sequences

In this paper we propose a new method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It can handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.

Pose Invariant Shape Prior Segmentation Using Continuous Cuts and Gradient Descent on Lie Groups

This paper proposes a novel formulation of the Chan-Vese model for pose invariant shape prior segmentation as a continuous cut problem. The model is based on the classic L 2 shape dissimilarity measure and with pose invariance under the full (Lie-) group of similarity transforms in the plane. To overcome the common numerical problems associated with step size control for translation, rotation and scaling in the discretization of the pose model, a new gradient descent procedure for the pose estimation is introduced. This procedure is based on the construction of a Riemannian structure on the group of transformations and a derivation of the corresponding pose energy gradient. Numerically, this amounts to an adaptive step size selection in the discretization of the gradient descent equations. Together with efficient numerics for TV-minimization we get a fast and reliable implementation of the model. Moreover, the theory introduced is generic and reliable enough for application to more general segmentation- and shape-models.

Variational segmentation of image sequences using deformable shape priors

The segmentation of objects in image sequences is an important and difficult problem in computer vision with applications to e.g. video surveillance. In this paper we propose a new method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the classical Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It is expected to handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on real image sequences.

Rayleigh segmentation of the endocardium in ultrasound images

In this paper we present the coupled active contours (CAC) model, which is applied to segmentation of the endocardium in ultrasonic images assuming Rayleigh distributed intensities. Comparative experiments, both real and synthetic, with a standard prior model are presented. In the CAC model the prior acts, by affine transformation, on the same image information as the active contour, in addition to the traditional interaction between prior and active contour. By this higher convergence rate and robustness, w.r.t artifacts and poor initialization, is achieved.