Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
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
Geodesic Active Contours Applied to Texture Feature Space
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics
Foundations of Computational Mathematics
Towards recognition-based variational segmentation using shape priors and dynamic labeling
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
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
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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
International Journal of Computer Vision
Shape-Based Mutual Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Active Contours without Edges and with Simple Shape Priors
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Combining Shape Priors and MRF-Segmentation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multi-Reference Shape Priors for Active Contours
International Journal of Computer Vision
Cooperative Object Segmentation and Behavior Inference in Image Sequences
International Journal of Computer Vision
Simultaneous object classification and segmentation with high-order multiple shape models
IEEE Transactions on Image Processing
Multiphase active contour segmentation constrained by evolving medial axes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiple shape models for simultaneous object classification and segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A multiphase region-based framework for image segmentation based on least square method
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Model-based recognition of domino tiles using TGraphs
Proceedings of the 32nd DAGM conference on Pattern recognition
Morphology-guided graph search for untangling objects: C. elegans analysis
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
An effective segmentation for noise-based image verification using gamma mixture models
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Piecewise constant level set methods and image segmentation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Reduced set density estimator for object segmentation based on shape probabilistic representation
Journal of Visual Communication and Image Representation
Model-based recognition of 2D objects under perspective distortion
Pattern Recognition and Image Analysis
International Journal of Computer Vision and Image Processing
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We propose a variational framework for the integration of multiple competing shape priors into level set based segmentation schemes. By optimizing an appropriate cost functional with respect to both a level set function and a (vector-valued) labeling function, we jointly generate a segmentation (by the level set function) and a recognition-driven partition of the image domain (by the labeling function) which indicates where to enforce certain shape priors. Our framework fundamentally extends previous work on shape priors in level set segmentation by directly addressing the central question of where to apply which prior. It allows for the seamless integration of numerous shape priors such that--while segmenting both multiple known and unknown objects--the level set process may selectively use specific shape knowledge for simultaneously enhancing segmentation and recognizing shape.