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» Learning Probabilistic Models of Contours
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ICPR
2010
IEEE
15 years 1 months ago
Initialisation-Free Active Contour Segmentation
We present a region based active contour model which does not require any initialisation and is capable of modelling multi-modal image regions. Its external force is based on stat...
Xianghua Xie, Majid Mirmehdi
ICPR
2006
IEEE
15 years 11 months ago
Shape Alignment by Learning a Landmark-PDM Coupled Model
This paper revisits the model-based approaches for groupwise shape alignment. The key contribution is modeling the landmarks instead of considering them as nodes sliding along the...
Yifeng Jiang, Jun Xie, Hung-Tat Tsui
ICLP
2009
Springer
15 years 10 months ago
Generative Modeling by PRISM
PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic modeling capable of learning statistical parameters from observed data. After reviewing ...
Taisuke Sato
ICML
2009
IEEE
15 years 10 months ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
ECML
2006
Springer
15 years 1 months ago
Bayesian Learning of Markov Network Structure
Abstract. We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend na
Aleks Jakulin, Irina Rish