Connected image processing with multivariate attributes: An unsupervised Markovian classification approach

Connected image processing with multivariate attributes: An unsupervised Markovian classification approach

This article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, ...

Unsupervised parallel image classification using Markovian models1

This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label fiel...

Unsupervised parallel image classification using Markovian models

Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model∗

This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc. . . ). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observabl...

An evidential Markovian model for data fusion and unsupervised image classification