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Nonlinear Shape Statistics in MumfordShah Based Segmentation
 In European Conference on Computer Vision
, 2002
"... We present a variational integration of nonlinear shape statistics into a MumfordShah based segmentation process. The nonlinear 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 fra ..."
Abstract

Cited by 68 (8 self)
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We present a variational integration of nonlinear shape statistics into a MumfordShah based segmentation process. The nonlinear 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.
Statistical Shape Knowledge in Variational Motion Segmentation
 IMAGE AND VISION COMPUTING
, 2002
"... We present a generative approach to modelbased motion segmentation by incorporating a statistical shape prior into a novel variational segmentation method. The shape prior statistically encodes a training set of object outlines presented in advance during a training phase. In a region ..."
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Cited by 25 (3 self)
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We present a generative approach to modelbased motion segmentation by incorporating a statistical shape prior into a novel variational segmentation method. The shape prior statistically encodes a training set of object outlines presented in advance during a training phase. In a region
Nonlinear Shape Statistics via Kernel Spaces
 Pattern Recognition, volume 2191 of LNCS
, 2001
"... We present a novel approach for representing shape knowledge in terms of example views of 3D objects. Typically, such data sets exhibit a highly nonlinear structure with distinct clusters in the shape vector space, preventing the usual encoding by linear principal component analysis (PCA). For this ..."
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Cited by 11 (2 self)
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We present a novel approach for representing shape knowledge in terms of example views of 3D objects. Typically, such data sets exhibit a highly nonlinear structure with distinct clusters in the shape vector space, preventing the usual encoding by linear principal component analysis (PCA). For this reason, we propose a nonlinear Mercer kernel PCA scheme which takes into account both the projection distance and the withinsubspace distance in a highdimensional feature space. The comparison of our approach with supervised mixture models indicates that the statistics of example views of distinct 3D objects can fairly well be learned and represented in a completely unsupervised way.
Image Segmentation using MRFs and Statistical Shape Modeling
, 2010
"... In this thesis, we introduce a new statistical shape model and use it for knowledgebased image segmentation. The model is represented by a Markov Random Field (MRF). The vertices of the graph correspond to landmarks lying on the shape boundary, whereas the edges of the graph encode the dependencie ..."
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Cited by 1 (0 self)
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In this thesis, we introduce a new statistical shape model and use it for knowledgebased image segmentation. The model is represented by a Markov Random Field (MRF). The vertices of the graph correspond to landmarks lying on the shape boundary, whereas the edges of the graph encode the dependencies between the landmarks. The MRF structure is determined from a training set of shapes using manifold learning and unsupervised clustering techniques. The interpoint constraints are enforced using the learned probability distribution function of the normalized chord lengths. This model is used as a basis for knowledgebased segmentation. We adopt two approaches to incorporate the data support: one is based on landmark correspondences and the other one uses image region information. In the first case, correspondences between the model and the image are obtained through detectors and the optimal configuration is achieved through combination of detector responses and prior knowledge. The second approach consists of minimizing an energy that discriminates the object from the background while