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

Authors:
Ketut Fundana;Niels C. Overgaard;Anders Heyden
Affiliations:
Applied Mathematics Group, School of Technology, Malmö University, Malmö, Sweden;Applied Mathematics Group, School of Technology, Malmö University, Malmö, Sweden;Applied Mathematics Group, School of Technology, Malmö University, Malmö, Sweden
Venue:
International Journal of Computer Vision
Year:
2008

Citing 20
Cited 1

Geodesic Active Contours

International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shapes and geometries: analysis, differential calculus, and optimization

Shapes and geometries: analysis, differential calculus, and optimization
Using Prior Shapes in Geometric Active Contours in a Variational Framework

International Journal of Computer Vision
Shape Priors for Level Set Representations

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geodesic active regions and level set methods for motion estimation and tracking

Computer Vision and Image Understanding
Level Set Based Shape Prior Segmentation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation

International Journal of Computer Vision
A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion

International Journal of Computer Vision
Prior Knowledge, Level Set Representations & Visual Grouping

International Journal of Computer Vision
Segmentation under occlusions using selective shape prior

SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Variational segmentation of image sequences using deformable shape priors

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Efficient kernel density estimation of shape and intensity priors for level set segmentation

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A geometric formulation of gradient descent for variational problems with moving surfaces

Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Active contours without edges

IEEE Transactions on Image Processing
Segmentation for robust tracking in the presence of severe occlusion

IEEE Transactions on Image Processing

Reduced set density estimator for object segmentation based on shape probabilistic representation

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.