Christophe Collet

Sonar image segmentation using an unsupervised hierarchical MRF model

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.

Three-Class Markovian Segmentation of High-Resolution Sonar Images

This paper presents an original method for analyzing, in an unsupervised way, images supplied by high resolution sonar. We aim at segmenting the sonar image into three kinds of regions: echo areas (due to the reflection of the acoustic wave on the object), shadow areas (corresponding to a lack of acoustic reverberation behind an object lying on the sea-bed), and sea-bottom reverberation areas. This unsupervised method estimates the parameters of noise distributions, modeled by a Weibull probability density function (PDF), and the label field parameters, modeled by a Markov random field (MRF). For the estimation step, we adopt a maximum likelihood technique for the noise model parameters and a least-squares method to estimate the MRF prior model. Then, in order to obtain an accurate segmentation map, we have designed a two-step process that finds the shadow and the echo regions separately, using the previously estimated parameters. First, we introduce a scale-causal and spatial model called SCM (scale causal multigrid), based on a multigrid energy minimization strategy, to find the shadow class. Second, we propose a MRF monoscale model using a priori information (at different level of knowledge) based on physical properties of each region, which allows us to distinguish echo areas from sea-bottom reverberation. This technique has been successfully applied to real sonar images and is compatible with automatic processing of massive amounts of data.

Hybrid genetic optimization and statistical model based approach for the classification of shadow shapes in sonar imagery

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.

Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps

This paper presents an unsupervised method to segment multispectral images, involving a correlated non-Gaussian noise. The efficiency of the Markovian quadtree-based method we propose will be illustrated on a satellite image segmentation task with multispectral observations, in order to update nautical charts. The proposed method relies on a hierarchical Markovian modeling and includes the estimation of all involved parameters. The parameters of the prior model are automatically calibrated while the estimation of the noise parameters is solved by identifying generalized distribution mixtures [P. Rostaing, J.-N. Provost, C. Collet, Proc. International Workshop EMMCVPR99: Energy Minimisation Methods in Computer Vision and Pattern Recognition, Springer Verlag, New York, 1999, p. 141], by means of an iterative conditional estimation (ICE) procedure. Generalized Gaussian (GG) distributions are considered to model various intensity distributions of the multispectral images. They are indeed well suited to a large variety of correlated multispectral data. Our segmentation method is applied to Satellite Pour lObservation de la Terre (SPOT) remote multispectral images. Within each segmented region, a bathymetric inversion model is then estimated to recover the water depth map. Experiments on different real images have demonstrated the efficiency of the whole process and the accuracy of the obtained results has been assessed using ground truth data. The designed segmentation method can be extended to images for which it is required to segment a region of interest using an unsupervised approach.

Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation

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

Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery

This work deals with unsupervised sonar image segmentation. We present a new estimation and segmentation procedure on images provided 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 seabed) and reverberation (due to the re#ection of acoustic wave on the seabed and on the objects). The unsupervised contextual method we propose is dened as a two-step process. Firstly, the iterative conditional estimation is used for the estimation step in order to estimate the noise model parameters and to accurately obtain the proportion of each class in the maximum likelihood sense. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the input image to approximate the discriminating functions, i.e. the contextual distribution function of the grey levels. Secondly, the previously estimated proportion, the contextual information and the Kohonen SOM, after learning, are then used in the segmentation step in order to classify each pixel on the input image. This technique has been successfully applied to real sonar images, and is compatible with an automatic processing of massive amounts of data. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Hierarchical MRF modeling for sonar picture segmentation

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 Rayleighs 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.

Multiband segmentation based on a hierarchical Markov model

We develop a new multiscale Markov segmentation model for multiband images. Using quadtree multiple resolution analysis of a multiband image, we use both inter- and intra-scale spatial Markov statistical dependencies. Bayesian inference is used to assess the appropriate number of segments. We exemplify the excellent results which can be obtained with this approach using synthetic images, and in two case studies involving multiband astronomical image sets.

Unsupervised Markovian segmentation of sonar images

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.

Markov Random Field and Fuzzy Logic Modeling in Sonar Imagery

This paper proposes an original method for the classification of seafloors from high resolution sidescan sonar images. We aim at classifying the sonar images into five kinds of regions: sand, pebbles, rocks, ripples, and dunes. The proposed method adopts a pattern recognition approach based on the extraction and the analysis of the cast shadows exhibited by each seabottom type. This method consists of three stages of processing. First, the original image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each “object” lying on the seabed) and seabottom reverberation. Second, based on the extracted shadows, shape parameter vectors are computed on subimages and classified with a fuzzy classifier. This preliminary classification is finally refined thanks to a Markov random field model which allows to incorporate spatial homogeneity properties one would expect for the final classification map. Experiments on a variety of real high-resolution sonar images are reported.