Dynamical statistical shape priors for level set based sequence segmentation

Authors:
Daniel Cremers;Gareth Funka-Lea
Affiliations:
Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ;Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ
Venue:
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Year:
2005

Citing 14
Cited 5

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of parameters and eigenmodes of multivariate autoregressive models

ACM Transactions on Mathematical Software (TOMS)
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
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
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Gradient flows and geometric active contour models

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Shape Representation via Harmonic Embedding

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing

Dynamical Statistical Shape Priors for Level Set-Based Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Preferential image segmentation using trees of shapes

IEEE Transactions on Image Processing
A variational framework for the simultaneous segmentation and object behavior classification of image sequences

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
4D shape priors for a level set segmentation of the left myocardium in SPECT sequences

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has been on statistical shape priors that are time-invariant. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated into a segmentation process in a Bayesian framework for image sequence segmentation. Experiments demonstrate that such shape priors with memory can drastically improve the segmentation of image sequences.