A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Digital Picture Processing
Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Boundary Finding with Correspondence Using Statistical Shape Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Statistical shape analysis for image segmentation and physical model-based nonrigid registration
Statistical shape analysis for image segmentation and physical model-based nonrigid registration
A Unified Feature Registration Method for Brain Mapping
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating prior shape models into level-set approaches
Pattern Recognition Letters
Geometric Analysis of Particle Motion in a Vector Image Field
Journal of Mathematical Imaging and Vision
Layered Cooperation of Macro Agents and Micro Agents in Cooperative Active Contour Model
Agent Computing and Multi-Agent Systems
Volumetric Shape Model for Oriented Tubular Structure from DTI Data
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Localized maximum entropy shape modelling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Subject specific shape modeling with incremental mixture models
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
A bayesian approach to image-based visual hull reconstruction
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Reinforcement learning for context aware segmentation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Subject-specific cardiac segmentation based on reinforcement learning with shape instantiation
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Coupled shape distribution-based segmentation of multiple objects
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape, and combined models, respectively. Indeed, snakes, modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available.