Unsupervised multiband image segmentation using hidden Markov quadtree and copulas

被引：0

作者：

Flitti, F ^{[1
]}

Collet, C ^{[1
]}

Joannic-Chardin, A ^{[1
]}

机构：

[1] Univ Strasbourg 1, LSIIT, UMR 7005, CNRS, F-67070 Strasbourg, France

来源：

2005 International Conference on Image Processing (ICIP), Vols 1-5 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper deals with Hidden Markov Quadtree model for multiband image segmentation. This task, requiring multivariate probability density computations for the data likelihood term, is often confronted with the lack of analytical multidimensional expressions in the non-gaussian case. Thus, multidimensional Gaussian distribution is usually used for its simplicity, even if Gaussian assumption is not always verified. In this work, we propose a new approach based on copula theory to compute multivariate density on Markov Quadtree.

引用

页码：1821 / 1824

页数：4

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