A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Automated pivot location for the Cartesian-Polar hybrid point distribution model
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes
Graphical Models and Image Processing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Nonlinear Modeling of Scattered Multivariate Data and Its Application to Shape Change
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curves and Surfaces for Computer-Aided Geometric Design: A Practical Code
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Diffusion-Snakes Using Statistical Shape Knowledge
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
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Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization
Proceedings of the 24th DAGM Symposium on Pattern Recognition
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Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
International Journal of Computer Vision
Robust Image Segmentation Using Resampling and Shape Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Locating and Segmenting 3D Deformable Objects by Using Clusters of Contour Fragments
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Joint Parametric and Non-parametric Curve Evolution for Medical Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Pre-image as Karcher Mean Using Diffusion Maps: Application to Shape and Image Denoising
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors
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Graph cut optimization for the Mumford-Shah model
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Joint brain parametric T1-map segmentation and RF inhomogeneity calibration
Journal of Biomedical Imaging
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MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
From Images to Shape Models for Object Detection
International Journal of Computer Vision
A multiresolution flow-based multiphase image segmentation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
ASM driven snakes in rheumatoid arthritis assessment
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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
Towards segmentation based on a shape prior manifold
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Active-contour-based image segmentation using machine learning techniques
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
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ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Computer Methods and Programs in Biomedicine
Diffusion maps as a framework for shape modeling
Computer Vision and Image Understanding
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VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Comparative analysis of kernel methods for statistical shape learning
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Image and Vision Computing
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Image and Vision Computing
Level set evolution with locally linear classification for image segmentation
Pattern Recognition
Graph cut segmentation with a statistical shape model in cardiac MRI
Computer Vision and Image Understanding
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We present a variational integration of nonlinear shape statistics into a Mumford-Shah based segmentation process. The non-linear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic framework.The idea is to assume that the training data forms a Gaussian distribution after a nonlinear mapping to a potentially higher-dimensional feature space. Due to the strong nonlinearity, the corresponding density estimate in the original space is highly non-Gaussian. It can capture essentially arbitrary data distributions (e.g. multiple clusters, ring- or banana-shaped manifolds).Applications of the nonlinear shape statistics in segmentation and tracking of 2D and 3D objects demonstrate that the segmentation process can incorporate knowledge on a large variety of complex real-world shapes. It makes the segmentation process robust against misleading information due to noise, clutter and occlusion.