Hidden Markov Trees (HMTs) are successful probabilistic models [1] [2] [3] in image segmentation or genetic analysis for example. They offer a good compromise between the random variables that can be modeled and the tractability of the inference within these models. Indeed, the inference procedures can be conducted in a direct, exact fashion [4]. However the conditional independence restrictions in HMTs can be too simplistic with respect to the interactions we wish to model, particularly in the task of natural image segmentation. In this article we study an extension of HMTs called Spatial Triplet Markov Trees (STMTs) which is designed to greatly increase the correlations of the random variables while keeping the possibility of direct and exact inference procedures. The STMT model is tested and compared to other unsupervised segmentation techniques in the case of unsupervised image segmentation. Numerical experiments show that STMTs outperform HMTs and present rich spatial dependencies which are crucial for image segmentation.

Hidden Markov Field modeling is widely used for image segmentation. However, it sometimes lacks power to handle complex situations, e.g. correlated noise, textures or non-stationarities. This is why Pairwise, and then Triplet Markov Fields were introduced to handle in a generic fashion more complex observations. In this paper, we tackle the problem of anisotropic image modeling by introducing an Oriented Triplet Markov Field model, able to explicitly deal with oriented structures. Using oriented features in the framework of Triplet Markov Field modeling, we compare the behavior of this model towards other Markovian modeling on images containing such oriented pattern. We present experiments on synthetic data for segmentation, and application to real data from remote sensing images.

Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.

Random field geometry has proven relevant results in the context of statistical hypothesis test for solving detection problems in signal and image processing. This paper emphasizes an unsupervised target detection problem in hyperspectral noisy images with very low signal-to-noise ratio (SNR) conditions. The targets have unknown spectral signatures located at unknown bandwidths and positions. To this aim, a spatio-spectral Gaussian random field (SS-GRF) model is proposed to provide a statistical inference about these targets in the full hyperspectral space by means of the geometric features of the noise, notably the expected Euler-characteristic (EC). The performance of the proposed method is demonstrated by the ROC curve analysis on synthetic examples, and confirms its efficiency and capacity to detect hyperspectral targets (astrophysical objects, remote sensing targets). At the end, we discuss the impact of the spectral dimensions on the method.

This paper introduces a novel approach for the automated detection of rectangular buildings from monocular very high resolution (VHR) aerial images. The overall idea of this work is first to decompose the image into small homogeneous regions and treat all regions as candidates. According to the position of the shadows, a merging process is then performed over regions having similar spectral characteristics to produce building regions whose shapes are appropriate to rectangles. The experimental results prove that the proposed method is applicable in various areas (high dense urban, suburban, and rural) and is highly robust and reliable.

This paper presents a new method for simultaneously detecting shadows and vegetation in remote sensing images, based on Otsu's thresholding method and Dempster-Shafer (DS) fusion which aims at combining different shadow indices and vegetation indices in order to increase the information quality and to obtain a more reliable and accurate segmentation result. The DS fusion is carried out pixel by pixel and is incorporated in the Markovian context while obtaining the optimal segmentation with the energy minimization scheme associated with the MRF. This new approach is applied on remote sensing images and demonstrates its efficiency.

In order to maintain and improve the quality of life of elderly and people with cognitive impairments in their home, we must elaborate assistive technologies that will alleviate the effects of cognitive decline. The use of ambient intelligence, such as smart homes, is a way to provide assistance services. However, managing these services is not a trivial task. In this paper, we propose an organizational centered multi-agent system (OCMAS) architecture in order to manage these services in a smart home. This approach allows to ease the management of services and the deployment of new services in the smart home system dynamically.

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

This paper aims at classifying changed from unchanged pattern in multi-acquisition data using kernel based support vector data description (SVDD). Indeed, SVDD is a well known method allowing to map the data into a high dimensional features space where an hypersphere encloses most patterns belonging to the ”un-changed” class. In this work, we propose a new kernel function which combines the characteristics of basic kernel functions with new information about features distribution and then dependency between samples through copula theory that will be used for the first time to our knowledge in the SVDD framework. The effectiveness of the method is demonstrated on synthetic and real data sets.

In this paper, we investigate the possibilities offered by the extension of the connected component trees (cc-trees) to multivariate images. We propose a general framework for image processing using the cc-tree based on the lattice theory and we discuss the possible applications depending on the properties of the underlying ordered set. This theoretical reflexion is illustrated by two applications in multispectral astronomical imaging: source separation and object detection.

A wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing, commonly experienced by persons suffering from asthma. In this paper, we present a new method for the detection of wheezing sounds in the normal breathing sounds. In our study we perform an accurate statistical analysis of breathing signals. We suggest a modeling for wheezing and normal sounds in the wavelet packet domain using generalized Gaussian distributions. Our detection method is based on a specific multimodal Markovian modeling proposed in a Bayesian framework. We cope with the multidimensional aspect of the generalized Gaussian distribution by using the theory of copulas. Experimental results are given in detail in this paper.

In this paper, we present a robust method to estimate parameters of hidden Markov chains (HMC) in order to segment brain MR images. Indeed, parameter estimation can be very sensitive to the presence of outliers in the data. We propose to use the trimmed likelihood estimator (TLE) to extract such outliers and to accurately estimate the parameters of different tissue classes in a robust way. Moreover neighborhood information is included in the model by using hidden Markov chains. Experimental results on 2D synthetic data and on 3D brain MRI are included to validate this approach.

In this paper we propose a statistical modeling approach for phase detection of normal breathing sounds. Previous studies have been considering only the detection of inspiration mid-points and breathing onset. Here we focus on the detection of both inspiration and expiration phases. Based on an accurate statistical study of breathing signals, we suggest a nomenclature of respiratory cycle in a modeling perspective by adding a transitional phase between the inspiration and expiration phases. Thus, we put forward a new processing chain using improved Markov model in a bayesian framework in order to segment the signal and to detect the phases. We adapt the recent triplet Markov chain by exploiting priors on the respiratory cycle structure. Experiments on real respiratory signals show encouraging results.

This paper proposes a new method to detect multiple sclerosis (MS) lesions on 3D multimodal brain MR images. MS lesions are detected as voxels that are not well explained by a statistical model for normal brain images. These outliers are extracted using the trimmed likelihood estimator (TLE). Spatial regularization is performed using a hidden Markov chain (HMC) model. Tests on real brain MR images with MS lesions have been carried out and results have been compared to manual expert segmentation to validate the proposed method.

This paper is concerned with multi-component image segmentation which plays an important role in many imagery applications. Unfortunately, we are faced with the Hughes phenomenon when the number of components increases, and a space dimensionality reduction is often carried out as a preprocessing step before segmentation. An interesting solution is the mixtures of latent variable models which recover clusters in the observation structure and establish a local linear mapping on a reduced dimension space for each cluster. Thus, a globally nonlinear model is obtained to reduce dimensionality. Furthermore, a likelihood to each local model is often available which allows a well formulation of the mixture model and a maximum likelihood based decision for the clustering task. However for D-component images classification, such clustering, based only on the distance between observations in the D-dimensional space is not adapted since it neglects the observation spatial locations in the image. We propose to use a Markov a priori associated with such models to regularize D-dimensional pixel classification. Thus segmentation and reduction are performed simultaneously. In this paper, we focus on the probabilistic principal component analysis (PPCA) as latent model, and the hidden Markov quad-tree (HMT) as a Markov a priori

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

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.

This paper deals with the unsupervised segmentation of astronomical multiband images. Most of these images have the particularity to be quantized on float numbers with large luminance range, on different wavelengths. These characteristics require to manipulate large amount of extremely accurate data on each spectral band, which is very different to the case of 8-bits-integer coded pixels. We present some results obtained on multiwavelength images of the small magellanic cloud, by using the marginal posterior mode (MPM) estimator on a quadtree structure under Markovian assumption. The estimation of the model parameters is then addressed with expectation-maximization (EM)-type algorithms, allowing unsupervised hyperparameter estimation. The main interest of this modeling effort lies in its generality: the algorithm handles multiwavelength floating data in a single upward and downward scan on the quadtree. A new aspect in this paper concerns the noise statistics that are supposed to be lognormal for each class. Another new aspect, is the in-scale-coding of the label map.

This paper presents the determination of bathymetric maps from the analysis of multispectral SPOT images. To this end, we have developed a multispectral segmentation method based on a hierarchical Markovian modeling including the unsupervised estimation of the model parameters. In each segmented region, an adaptive bathymetric inversion model is then applied in order to recover the water depth (mainly in coastal areas). Bathymetric estimation has been validated on real data, for which control points are available and correspond to bathymetric measures supplied by previous hydrographic campaigns.

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results.

This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called the scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation.

This paper presents an unsupervised image segmentation method with applications to ocean cartography. By using SPOT satellite data, the aim is to improve the automatic production of bathymetric charts. Indeed, in coastal areas, satellite images provide the radiometry of the electromagnetic waves backscattered by the vegetation, the sea or the sea floor depending on the sea depth in littoral areas. The proposed segmentation method is based on a hierarchical Markovian model defined on a quad-tree, combining multispectral data. One of its interests is to take explicitly into account the correlation between the three spectral channels of the observation. Classification results obtained with synthetic and real images demonstrate the efficiency of the method.

We present a new classification method using a deformable template model to separate natural objects from man made objects in an image given by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is described by a prototype template and a set of admissible linear transformations to take into account the shape variability. Then, the classification problem is defined as a two step process; firstly the detection problem of a region of interest in the input image is stated in a Bayesian framework and is posed as an equivalent energy minimization problem of an objective function: in this paper, this energy minimization problem is solved by using a hybrid genetic algorithm (GA). Secondly, the value of this function at convergence allows one to determine the presence of the desired object in the sonar image. This method has been successfully tested on real and synthetic sonar images, yielding very promising results.

This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the an iterative method called iterative conditional estimation (ICE). This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label field (modeled by a Markov random field (MRF)). For the estimation step we use a maximum likelihood estimation for the noise model parameters and the least square method proposed by Derin et al. (1987) to estimate the MRF prior model. Then, in order to obtain a good segmentation and to speed up the convergence rate, we use a multigrid strategy with the previously estimated parameters. This technique has been successfully applied to real sonar images and is compatible with an automatic treatment of massive amounts of data.

This paper deals with sonar image segmentation based on a hierarchical Markovian modeling. The designed Markov random field (MRF) model takes into account both the phenomenon of speckle noise through Rayleigh's law, and notions of geometry related to the shape of object shadows. We adopt an 8-connexity neighbourhood in order to discriminate geometric and non-regular shadows. MRF are well adapted for this kind of segmentation where a priori knowledge about the shapes we are searching is available. Besides, the introduced hierarchical modeling allows us to successfully improve the sonar image segmentation while speeding up the iterative optimization scheme.

In a surveillance system in an aerospace environment which has the task of detecting a source a long distance away and thus has a very low resolution, cloudy sky velocity estimation should allow a lower rate of false alerts in discriminating the motion between the different patterns. This kinematic field estimation with little apparent motion could be effective by using the generalized Hough transform method or with a differential method based on optical flow by which one should locally obtain the kinematic field estimation. The paper aims at showing and adapting this differential method in areas where there is motion. These areas are detected by means of an entropy criterion. The method can discern the global movement of objects whatever their form. A local kinematic vector estimation method, based on the least mean squares is then tested.>