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» Discrete Mixture Models for Unsupervised Image Segmentation
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CVPR
1999
IEEE
16 years 1 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
16 years 1 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»
14 years 9 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
15 years 5 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
15 years 24 days 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