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» Discrete Mixture Models for Unsupervised Image Segmentation
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CVPR
1999
IEEE
14 years 9 months ago
Deformable Template and Distribution Mixture-Based Data Modeling for the Endocardial Contour Tracking in an Echographic Sequence
We1 present a new method to shape-based segmentation of deformable anatomical structures in medical images and validate this approach by detecting and tracking the endocardial bor...
Max Mignotte, Jean Meunier
ICIP
2004
IEEE
14 years 9 months ago
Unsupervised motion detection using a markovian temporal model with global spatial constraints
In this work, we propose an unsupervised Bayesian model for the detection of moving objects from dynamic scenes. This unsupervised solution is a three-step approach that uses a st...
Pierre-Marc Jodoin, Max Mignotte
ICMCS
2009
IEEE
104views Multimedia» more  ICMCS 2009»
13 years 5 months ago
A variational multi-view learning framework and its application to image segmentation
The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the g...
Zhenglong Li, Qingshan Liu, Hanqing Lu
PCI
2005
Springer
14 years 1 months ago
Unsupervised Learning of Multiple Aspects of Moving Objects from Video
A popular framework for the interpretation of image sequences is based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2]. Jojic and Frey [3] provide a generativ...
Michalis K. Titsias, Christopher K. I. Williams
DAGM
2010
Springer
13 years 8 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen