Segmentation non supervisée des images par arbres de Markov couple

Nous traitons dans cet article de la segmentation statistique non supervisée d'images de synthèse en utilisant le modèle récent des arbres de Markov couple. L'objectif de cet article est de montrer que la stricte généralisation du modèle des arbres de Markov cachés apporte, notamment dans le cas non supervisé où un algorithme original de type ICE est proposé, un gain appréciable au niveau de la qualité de la segmentation. Les exemples traités montrent en effet que le modèle des diarbres de Markov couple permet d'améliorer les résultats obtenus pour les diarbres de Markov cachés

Generalized mixture estimation in hidden Markov trees, application to segmentation of images of street organ cards

We deal in this paper with unsupervised statistical image segmentation using hidden Markov trees. First, we propose two original evolutionary models and study, via simulations, the behaviour of different general estimation methods. Second, we propose a new generalized mixture estimation method and show its efficiency in unsupervised image segmentation, even in very noisy settings. The proposed method is then successfully applied to the unsupervised segmentation of street organ cards images.Nous nous intéressons dans cet article à la segmentation statistique non supervisée d'images avec les modèles par arbres de Markov cachés. Dans un premier temps nous proposons deux modèles évolutifs originaux et étudions, via simulations, le comportement des diverses méthodes générales de l'estimation des paramètres. Ensuite, nous proposons une méthode originale d'estimation de mélanges généralisés et montrons son bon comportement, même dans des cas d'images très fortement bruiteés, par une étude de simulations. Cette même méthode est appliquée au problème de la segmentation des cartons d'orgue de barbarie, attestant de son intérêt dans une situation réelle

The Kinesin‐3 motor, KLP‐4, mediates axonal organization and cholinergic signaling in Caenorhabditis elegans

Microtubule plus‐end directed trafficking is dominated by kinesin motors, yet kinesins differ in terms of cargo identity, movement rate, and distance travelled. Functional diversity of kinesins is especially apparent in polarized neurons, where long distance trafficking is required for efficient signal transduction‐behavioral response paradigms. The Kinesin‐3 superfamily are expressed in neurons and are hypothesized to have significant roles in neuronal signal transduction due to their high processivity. Although much is known about Kinesin‐3 motors mechanistically in vitro, there is little known about their mechanisms in vivo. Here, we analyzed KLP‐4, the Caenorhabditis elegans homologue of human KIF13A and KIF13B. Like other Kinesin‐3 superfamily motors, klp‐4 is highly expressed in the ventral nerve cord command interneurons of the animal, suggesting it might have a role in controlling movement of the animal. We characterized an allele of klp‐4 that contains are large indel in the cargo binding domain of the motor, however, the gene still appears to be expressed. Behavioral analysis demonstrated that klp‐4 mutants have defects in locomotive signaling, but not the strikingly uncoordinated movements such as those found in unc‐104/KIF1A mutants. Animals with this large deletion are hypersensitive to the acetylcholinesterase inhibitor aldicarb but are unaffected by exogenous serotonin. Interestingly, this large klp‐4 indel does not affect gross neuronal development but does lead to aggregation and disorganization of RAB‐3 at synapses. Taken together, these data suggest a role for KLP‐4 in modulation of cholinergic signaling in vivo and shed light on possible in vivo mechanisms of Kinesin‐3 motor regulation

Second-Order Belief Hidden Markov Models

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model

A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

International audienceAn 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

Rehabilitation of the Elbow Following Sports Injury

Work published in Clinics in Sports Medicine