Directional Estimation for Robotic Beating Heart Surgery

In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart

Directional Estimation for Robotic Beating Heart Surgery

In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

La localización y mapeado simultáneo a partir de una sola cámara en movimiento se conoce como Monocular SLAM. En esta tesis se aborda este problema con cámaras de bajo coste cuyo principal reto consiste en ser robustos al ruido, blurring y otros artefactos que afectan a la imagen. La aproximación al problema es discreta, utilizando solo puntos de la imagen significativos para localizar la cámara y mapear el entorno. La principal contribución es una simplificación del grafo de poses que permite mejorar la precisión en las escenas más habituales, evaluada de forma exhaustiva en 4 datasets. Los resultados del mapeado permiten obtener una reconstrucción 3D de la escena que puede ser utilizada en arquitectura y construcción para Modelar la Información del Edificio (BIM). En la segunda parte de la tesis proponemos incorporar dicha información en un sistema de visualización avanzada usando WebGL que ayude a simplificar la implantación de la metodología BIM.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unravelling the mechanisms of various diseases at the molecular level. The biggest challenge in both human- and computer-based visual analysis of micrographs is the variety of nanostructures and mitochondrial morphologies. The state-of-the-art is, however, dominated by supervised manual data annotation and early attempts to automate the segmentation process were based on supervised machine learning techniques which require large datasets for training. Given a minimal number of training sequences or none at all, unsupervised machine learning formulations, such as spectral dimensionality reduction, are known to be superior in detecting salient image structures. This thesis presents three major contributions developed around the spectral clustering framework which is proven to capture perceptual organization features. Firstly, we approach the problem of mitochondria localization. We propose a novel grouping method for the extracted line segments which describes the normal mitochondrial morphology. Experimental findings show that the clusters obtained successfully model the inner mitochondrial membrane folding and therefore can be used as markers for the subsequent segmentation approaches. Secondly, we developed an unsupervised mitochondria segmentation framework. This method follows the evolutional ability of human vision to extrapolate salient membrane structures in a micrograph. Furthermore, we designed robust non-parametric similarity models according to Gestaltic laws of visual segregation. Experiments demonstrate that such models automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions. The last major contribution addresses the computational complexity of spectral clustering. Here, we introduced a new anticorrelation-based spectral clustering formulation with the objective to improve both: speed and quality of segmentation. The experimental findings showed the applicability of our dimensionality reduction algorithm to very large scale problems as well as asymmetric, dense and non-Euclidean datasets

Courbure discrète : théorie et applications

International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor

Object recognition in infrared imagery using appearance-based methods

Abstract unavailable please refer to PD

Vision par ordinateur: outils fondamentaux

National audienceLa deuxième édition revue et augmentée de cet ouvrage présente les outils fondamentaux de la vision par ordinateur dans un langage mathématique accessible aux étudiants de niveau licence en mathématiques ou informatique. Il donne également de nombreux exemples d'utilisation de la vision par ordinateur dans deux domaines de technologie de pointe : la robotique et l'imagerie médicale. L'ouvrage est complété par 185 références bibliographiques commentées tout le long du texte

Segmentation of neuroanatomy in magnetic resonance images

Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account