Contextual Peano Scan and Fast Image Segmentation Using Hidden and Evidential Markov Chains

TL;DR

This paper introduces a novel evidential hidden Markov chain model combined with a contextual Peano scan for fast, unsupervised image segmentation, demonstrating improved effectiveness on synthetic and real images.

Contribution

It proposes a new HEMC-CPS model that integrates contextual Peano scan and evidential HMCs for enhanced Bayesian image segmentation.

Findings

Effective segmentation of synthetic and real images.

Potential for complex and multi-sensor image analysis.

Unsupervised parameter estimation using SEM.

Abstract

Transforming bi-dimensional sets of image pixels into mono-dimensional sequences with a Peano scan (PS) is an established technique enabling the use of hidden Markov chains (HMCs) for unsupervised image segmentation. Related Bayesian segmentation methods can compete with hidden Markov fields (HMFs)-based ones and are much faster. PS has recently been extended to the contextual PS, and some initial experiments have shown the value of the associated HMC model, denoted as HMC-CPS, in image segmentation. Moreover, HMCs have been extended to hidden evidential Markov chains (HEMCs), which are capable of improving HMC-based Bayesian segmentation. In this study, we introduce a new HEMC-CPS model by simultaneously considering contextual PS and evidential HMC. We show its effectiveness for Bayesian maximum posterior mode (MPM) segmentation using synthetic and real images. Segmentation is performed in an unsupervised manner, with parameters being estimated using the stochastic expectation--maximization (SEM) method. The new HEMC-CPS model presents potential for the modeling and segmentation of more complex images, such as three-dimensional or multi-sensor multi-resolution images. Finally, the HMC-CPS and HEMC-CPS models are not limited to image segmentation and could be used for any kind of spatially correlated data.