A Bayesian network for combining descriptors: application to symbol recognition

International audienceIn this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shapes measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach

Image Restoration from Multiple Sources

This paper proposes a new method of image restoration. The proposed method allows to combine information from several sources, taking the perceived credibility of each into account. It is applicable to both ordinal (e.g., gray level image) and non-ordinal (e.g., classified forest map) categorized images. The accuracy checks have shown the method to be robust with respect to the prior information and the accuracy of the sources. Two application examples are provided

Probabilistic partial volume modelling of biomedical tomographic image data

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Assessing the potential for suffusion in sands using x-ray micro-CT images

Internal erosion is a major safety concern for embankment dams and flood embankments and is the focus of much research internationally. Suffusion is a mechanism of internal erosion which affects gap-graded or broadly graded cohesionless soils and is characterised by selective removal of fine material, leaving behind a coarse material with increased hydraulic conductivity. Early studies on suffusion proposed design criteria based on laboratory testing, and presented conceptual models to explain the results in terms of grain-scale behaviour. The study by Kenney & Lau (1985) identified three criteria for suffusion: 1 – Fine particles must be free to move (mechanical criterion); 2 – Fine particles must be small enough to fit through the void space between coarse particles (geometric criterion); 3 – Fluid flowing through the void space must have sufficient velocity to transport the fine particles (hydraulic criterion). Recent studies have examined the first two criteria using grain-scale models with idealised particles, including analytical models and discrete element models (DEM) with circular or spherical particles. This thesis presents a new methodology, using non-destructive 3D imaging (micro-CT) to characterise the internal microstructure in physical specimens of sands and glass beads. This methodology involved the development of innovative image processing and numerical techniques to quantify unstable particle assemblies and to measure particle size distributions and void constriction size distributions. The new method was validated and was shown to produce good agreement with existing methods for idealised particle configurations, however the results for real sand specimens provided new insights into the effects of particle shape, particle size distribution and density on void constriction sizes. Furthermore, the 3D images of real specimens have provided new insights into the appropriateness of existing conceptual models for gap-graded particle structures. These results were used to critically examine and evaluate existing mechanical and geometric criteria for suffusion. The 3D images showed, qualitatively, that the void structures in sands varied significantly from those in porous rocks – which had been the basis for the majority of existing grain-scale fluid flow models. To examine this issue quantitatively, computational fluid dynamics (CFD) simulations were performed within the 3D images of sands and glass beads, in parallel to laboratory permeameter tests on the same materials. The results presented in this thesis provided entirely new insights into the patterns of fluid flow in sands, they allowed correlations to be made between fluid flow and void constriction sizes and also showed how local velocities varied from volume-average discharge and seepage velocities. This study provides new information to support, clarify and improve upon the current understanding of suffusion, filtration and seepage flows in sands. The detailed methodology and results also highlight issues of great importance to future micro-scale modelling of these phenomena.Open Acces

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg