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ICPR
2010
IEEE
13 years 11 months ago
A Semi-Supervised Gaussian Mixture Model for Image Segmentation
In this paper, the results of a semi-supervised approach based on the Expectation-Maximisation algorithm for model-based clustering are presented. We show in this work that, if th...
Adolfo Martínez-Usó, F. Pla, Jose Martínez Soto...
TSP
2008
105views more  TSP 2008»
13 years 9 months ago
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linea...
Nicolas Dobigeon, Jean-Yves Tourneret, Chein-I Cha...
CIARP
2006
Springer
14 years 1 months ago
Color Texture Segmentation by Decomposition of Gaussian Mixture Model
Abstract. Recently we have proposed Gaussian mixtures as a local statistical model to synthesize artificial textures. We describe the statistical dependence of pixels of a movable ...
Jiri Grim, Petr Somol, Michal Haindl, Pavel Pudil
ICIP
2005
IEEE
14 years 11 months ago
SAR images as mixtures of Gaussian mixtures
We consider the problem of image segmentation by clustering local histograms with parametric mixture-of-mixture models. These models represent each cluster by a single mixture mod...
Peter Orbanz, Joachim M. Buhmann
ICIAR
2010
Springer
13 years 6 months ago
Image Segmentation for Robots: Fast Self-adapting Gaussian Mixture Model
Image segmentation is a critical low-level visual routine for robot perception. However, most image segmentation approaches are still too slow to allow real-time robot operation. I...
Nicola Greggio, Alexandre Bernardino, José ...