logo EDITE Mohamed El Yazid BOUDAREN
Identité
Mohamed El Yazid BOUDAREN
État académique
Thèse soutenue le 2014-01-12
Sujet: Modèles Graphiques Evidentiels
Direction de thèse:
Encadrement de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:inria-00395327
Combining Extended Dependency Tree –HMM based Recognition and Unsupervised Segmentation for Land Cover Mapping in Aerial Images
An important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions. In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree –HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone.
International Conference of Signal and Image Engineering - ICSIE 2009proceeding with peer review 2009
oai:hal.archives-ouvertes.fr:inria-00579704
A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation
An important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions. In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree-HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone.
Lecture Notes in Electrical Engineeringscientific book chapter 2010-03-01
oai:hal.archives-ouvertes.fr:inria-00346632
Markov Models and Extensions for Land Cover Mapping in Aerial Imagery
Markov models are well-established stochastic models for image analysis and processing since they allow one to take into account the contextual relationships between image pixels. In this paper, we attempt to methodically review the use of Markov models and their extensions for Land Cover mapping problem in aerial imagery according to available literature and previous research works. A new Markov model combining Markov random fields and hidden Markov models and inspired from the NSHP-HMM model, initially introduced for Handwritten Words Recognition is defined. New learning and labeling procedures are derived.
International Conference of Signal and Image Engineering - ICSIE 2009proceeding with peer review 2009
oai:hal.archives-ouvertes.fr:inria-00579675
Extended Dependency Tree-HMM for Non-Rectangular Sub-Images Modeling
This work is motivated by the need of evaluating the likelihood probability on sub-images of not necessarily rectangular shape in some frameworks. For this purpose, we propose an alternative of Dependency Tree- HMM that allows the four traditional interactions between neighboring pixels instead of just two. To demonstrate the accuracy of the proposed model, we provide some classification results performed on high resolution aerial images.
The Fifth International Workshop on Applied Probability, IWAP 2010proceeding with peer review 2010-07-08
oai:hal.archives-ouvertes.fr:hal-00738142
Unsupervised segmentation of random discrete data hidden with switching noise distributions
Hidden Markov models are very robust and have been widely used in a wide range of application fields; however, they can prove some limitations for data restoration under some complex situations. These latter include cases when the data to be recovered are nonstationary. The recent triplet Markov models have overcome such difficulty thanks to their rich formalism, that allows considering more complex data structures while keeping the computational complexity of the different algorithms linear to the data size. In this letter, we propose a new triplet Markov chain that allows the unsupervised restoration of random discrete data hidden with switching noise distributions. We also provide genuine parameters estimation and MPM restoration algorithms. The new model is validated through experiments conducted on synthetic data and on real images, whose results show its interest with respect to the standard hidden Markov chain.
IEEE Signal Processing Lettersarticle in peer-reviewed journal 2012-10
oai:hal.archives-ouvertes.fr:hal-00766444
Dempster-Shafer fusion of multisensor signals in nonstationary Markovian context
The latest developments in the Markov models theory and their corresponding computational techniques have opened new avenues for image and signal modeling. In particular, the use of Dempster-Shafer theory of evidence within Markov models has brought some keys to several challenging difficulties that the conventional hidden Markov models cannot handle. These difficulties are concerned mainly with two situations: multisensor data, where the use of the Dempster-Shafer fusion is unworkable; and nonstationary data, due to the mismatch between the estimated model and the actual data. For each of the two situations, the Dempster-Shafer combination rule has been applied, thanks to the triplet Markov models formalism, to overcome the drawbacks of the standard Bayesian models. However, so far, both situations have not been considered in the same time. In this paper, we propose an evidential Markov chain that uses the Dempster-Shafer combination rule to bring the effect of contextual information into segmentation of multisensor nonstationary data. We also provide the EM- parameters estimation and MPM restoration procedures. To validate the proposed model, experiments are conducted on some synthetic multisensor data and noised images. The obtained segmentation results are then compared to those obtained with conventional approaches to bring out the efficiency of the present model.
EURASIP Journal on Advances in Signal Processingarticle in peer-reviewed journal 2012-07
oai:hal.archives-ouvertes.fr:hal-00778813
Unsupervised segmentation of nonstationary pairwise Markov chains using evidential priors
Hidden Markov models have been widely used to solve some inverse problems occurring in image and signal processing. These models have been recently generalized to pairwise Markov chains, which present higher modeling capabilities with comparable computational complexity. To be applicable in the unsupervised context, both models assume the data of interest stationary. When these latter are actually stationary, the models yield satisfactory results thanks to some Bayesian techniques such as MPM and MAP. However, when the data are nonstationary, they fail to establish an appropriate link with the data and the obtained results are quite poor. One interesting way to overcome this drawback is to use the Dempster-Shafer theory of evidence by introducing a mass function to model the lack of knowledge of the a priori distributions of the hidden data to be recovered. It has been shown that the use of such theory in the hidden Markov chains context yields significantly better results than those provided by the standard models. The aim of this paper is to apply the same theory in the pairwise Markov chains context to deal with nonstationary data hidden with correlated noise. We show that MPM restoration of data remains workable thanks to the triplet Markov models formalism. We also provide the corresponding parameters estimation in the unsupervised context. The new evidential model is then assessed through experiments conducted on synthetic and real images.
EUSIPCO '12 : 20th European Signal Processing Conference EUSIPCO '12 : 20th European Signal Processing Conference EUSIPCO '12 : 20th European Signal Processing Conferenceconference proceeding 2012
Soutenance
Thèse: Modèles graphique évidentiels.
Soutenance: 2014-01-12
Rapporteurs: Amina SERIR    Stéphane DERRODE