A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
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
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
A geometric criterion for shape-based non-rigid correspondence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
International Journal of Computer Vision
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Boundary Finding with Prior Shape and Smoothness Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Form representions and means for landmarks: a survey and comparative study
Computer Vision and Image Understanding
A Coupled Minimization Problem for Medical Image Segmentation with Priors
International Journal of Computer Vision
A variational formulation for segmenting desired objects in color images
Image and Vision Computing
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
Bayes Reconstruction of Missing Teeth
Journal of Mathematical Imaging and Vision
Principal Geodesic Analysis for the Study of Nonlinear Minimum Description Length
Medical Imaging and Informatics
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Reconstructing teeth with bite information
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
PCA based 3D shape reconstruction of human foot using multiple viewpoint cameras
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
A shape-based approach to robust image segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Hybrid framework for medical image segmentation
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Analyzing anatomical structures: leveraging multiple sources of knowledge
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Comparative analysis of kernel methods for statistical shape learning
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Graph cut segmentation with a statistical shape model in cardiac MRI
Computer Vision and Image Understanding
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We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.