A neuro-fuzzy monitoring system. Application to flexible production systems.

The multiple reconfiguration and the complexity of the modern production system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro-fuzzy technics seems very promising. In this paper, we propose a new monitoring aid system composed by a dynamic neural network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure permit us to update the monitoring aid system. The neuro-fuzzy network provides and abductive diagnosis. Moreover it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. At the end, we illustrate the industrial usefulness of the proposed dynamic neuro-fuzzy monitoring system through a flexible production system monitoring application

A fuzzy approach for discrete event systems recovery.

International audienceA fuzzy approach for modelling and analysing the recovery activities in discrete event systems is presented. Those essential components of the management of discrete event systems require special reasoning and methods to manage uncertain knowledge. For those purposes, we introduce a tool derived from the fuzzy Petri nets. This tool, inspired from the fault tree, generalizes the defects analysis by a temporal fuzzy approach. The recovery, modelled by a dedicated tool, preserves the fuzzy temporal aspect due to a real time information exchange mechanism provied by the monitoring system

Time-efficient sparse analysis of histopathological Whole Slide Images

International audienceHistopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology

Framework for neurosphere growth modelling under phase-contrast microscopy

L'étude des cellules souches est l'un des champs de recherches les plus importants dans le domaine biomédical. La vision par ordinateur et le traitement d'images ont été fortement mis en avant dans ce domaine pour le développement de solutions automatiques de culture et d'observation de cellules. Ce travail de thèse propose une nouvelle méthodologie pour l'observation et la modélisation de la prolifération de cellule souche neuronale sous microscope à contraste de phase. À chaque observation réalisée par le microscope durant la prolifération, notre système extrait un modèle en trois dimensions de la structure de cellules observées. Cela est réalisé par une suite de processus d'analyse, synthèse et sélection. Premièrement, une analyse de la séquence d'images de contraste de phase permet la segmentation de la neurosphère et des cellules la constituant. À partir de ces informations, combinées avec des connaissances a priori sur les cellules et le protocole de culture, plusieurs modèles 3-D possibles sont générés. Ces modèles sont finalement évalués et sélectionnés par rapport à l¿image d¿observation, grâce à une méthode de recalage 3-D vers 2-D. A travers cette approche, nous présentons un outil automatique de visualisation et d'observation de la prolifération de cellule souche neuronale sous microscope à contraste de phase.The study of stem cells is one of the most important fields of research in the biomedical field. Computer vision and image processing have been greatly emphasized in this area for the development of automated solutions for culture and observation of cells. This work proposes a new methodology for observing and modelling the proliferation of neural stem cell under a phase contrast microscope. At each time lapse observation performed by the microscope during the proliferation, the system determines a three-dimensional model of the structure formed by the observed cells. This is achieved by a framework combining analysis, synthesis and selection process. First, an analysis of the images from the microscope segments the neurosphere and the constituent cells. With this analysis, combined with prior knowledge about the cells and their culture protocol, several 3-D possible models are generated through a synthesis process. These models are finally selected and evaluated according to their likelihood with the microscope image using a 3-D to 2-D registration method. Through this approach, we present an automatic visualisation tool and observation of the proliferation of neural stem cell under a phase contrast microscope.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

Unsupervised dense crowd detection by multiscale texture analysis

International audienceThis study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised