Consulta: subjectFacets:"Classification supervisée"
Registros recuperados: 5
Data/hora: 02/06/2024 10:36:36

Version évolutive de l’outil opérationnel de numérisation et d’analyse semi-automatique d’images de phytoplancton, utilisant le matériel FlowCAM et le logiciel ZooPhytoImage. Nouvelles perspectives
Provedor de dados: 5
Autores: Wacquet, Guillaume; Grosjean, Philippe; Colas, Florent; Hamad, Denis; Artigas, Luis Felipe.
The coupled system FlowCAM/ZooPhytoImage has become a real operational tool in 2014. However, to be fully adapted to the observations of phytoplankton performed in the context of the REPHY observation network and in order to better respond to present and future requests concerning the evaluation of quality of coastal and marine waters within the European requirements, such as the WFD and MSFD, new functionalities must be integrated into existing tools. Therefore, different axis of development have been proposed by UMONS and Ifremer to adapt both the digitization device and the Zoo/PhytoImage software to the constraints defined by the REPHY. First, version 5 of Zoo/PhytoImage contains recent innovations such as the development of routines to automatically...
Tipo: Text
Palavras-chave: Plancton; Analyse automatisée; Analyse d'image; Classification supervisée; Apprentissage actif; Dénombrement de cellules; Plankton; Automated analysis; Image processing; Supervised classification; Active learning; Cells counting.
Ano: 2015
URL: http://archimer.ifremer.fr/doc/00389/49986/50573.pdf

Utilisation conjointe de FlowCAM / ZooPhytoImage et de la cytométrie en flux. Premiers résultats et perspectives. Action 9. FlowCam ZooPhytoImage. Livrable n° 4. Rapport final, 23 septembre 2014
Provedor de dados: 5
Autores: Ali, Nour; Wacquet, Guillaume; Didry, Morgane; Hamad, Denis; Artigas, Luis Felipe; Grosjean, Philippe.
The goal of this study is to investigate about the possibility of coupling measurements made by image analysis from the FlowCAM with Zoo/PhytoImage with data obtained with a flux cytometer (pulse-shape-recording Scanning Flow Cytometry) on the same samples gathered in current monitoring networks in the eastern Channel and southern North Sea. In this preliminary study, we collected a series of samples off Boulogne-sur-Mer (SRN-REPHY monitoring system run by IFREMER) and along a transect in the Baie St-Jean (Wimereux-Slack) run by LOG. All these samples were digitized with a FlowCAM and measured with a scanning flow cytometer (CytoSense). The complete analysis with the FlowCAM and Zoo/PhytoImage is detailed in the present report. In order to get a better...
Tipo: Text
Palavras-chave: Manche – Mer du Nord; Phytoplancton; Analyse d'image; Classification supervisée; Cytométrie en flux; Eastern English Channel and southern North Sea; Phytoplankton; Image analysis; Machine learning; Scanning Flow Cytometry.
Ano: 2014
URL: http://archimer.ifremer.fr/doc/00363/47442/47470.pdf

Protocole sur les outils de reconnaissance optimisés Manche Atlantique. Action 9 - FlowCam ZooPhytoImage. Livrable n° 2. Rapport final
Provedor de dados: 5
Autores: Wacquet, Guillaume; Lefebvre, Alain.
The FlowCAM/ZooPhytoImage tool is composed of FlowCAM device for digitizing the images of phytoplanktonic particles and ZooPhytoImage software that can automatically identify and count phytoplankton from these images. The latter is a software for image processing and automatic classification, based on the principle of "machine learning". It allows to realize the different steps of the process that leads to the automatic or semi-automatic classification of a set of objects from a given set of images, using supervised learning algorithms. For this, it is necessary to perform a training set composed of images from FlowCAM, representative of particles encountered in the samples to be analyzed later and make an automatic or semiautomatic recognition tool using...
Tipo: Text
Palavras-chave: Reconnaissance automatique; Set d'apprentissage; Classification supervisée; Validation croisée; Optimisation d'outils.; Automatic recognition; Training set; Supervised classification; Cross validation; Optimization of tools.
Ano: 2014
URL: http://archimer.ifremer.fr/doc/00363/47437/47463.pdf

Guide méthodologique. Version actualisée de ZooPhytoImage avec refonte de l’interface graphique. Action 9. FlowCam ZooPhytoImage. Livrable n°1. Rapport final
Provedor de dados: 5
Autores: Grosjean, Philippe.
Zoo/PhytoImage 4 is an « open source » software based on R and ImageJ. It processes numerical images of plankton particles digitized using a FlowCAM, a flat-bed scanner, microor macrophotos, etc. The general concept consists in the individual outlining of particles on the pictures, followed by their measurements (so-called « attributes ») such the size, the shape, transparency, textures, etc. These attributes are then used by a classification tool to automatically predict the taxonomic group the particles belong to. The classifier is obtained after a learning stage using a machine learning algorithm and a training set of pre-identified particles. The algorithm learns to recognize the taxonomic group from the set of attributes measured on the picture. The...
Tipo: Text
Palavras-chave: Océanographie biologique; Plancton; Surveillance côtière; Analyse automatisée; Analyse d'image; Classification supervisée; Biological oceanography; Plankton; Costal survey; Automated analysis; Image analysis; Machine learning.
Ano: 2014
URL: http://archimer.ifremer.fr/doc/00363/47436/47461.pdf

Optimisation de l’identification et du dénombrement du microphytoplancton avec le système couplé de numérisation et d’analyse d’images FlowCAM – Zoo/PhytoImage (système innovant)
Provedor de dados: 5
Autores: Grosjean, Philippe; Wacquet, Guillaume.
This report details the work accomplished to enhance the Zoo/PhytoImage software to optimize its use for the analysis of phytoplankton samples in general, but more particularly, in the framework of an operational survey of coastal seawater (REPHY, IFREMER). Zoo/PhytoImage allows to analyze “numerically recorded” plankton samples, that is, by using digital images gathered with specialized devices such as the FlowCAM, or the FastCAM (see report 3). A machine learning approach allows to automatically classify the digitized particles into various taxonomic groups. Once this is done, global statistics are calculated on each sample, including the number of particles, the biomass, and the size spectrum per taxonomic group. Two major changes are introduced in the...
Tipo: Text
Palavras-chave: Phytoplancton; REPHY; Analyse d'image; Classification supervisée; Dénombrement de cellules; Apprentissage actif; Manche; Atlantique.; Phytoplankton; REPHY; Image analysis; Machine learning; Cells enumeration; Active learning; The Channel; Atlantic Ocean.
Ano: 2016
URL: http://archimer.ifremer.fr/doc/00389/49990/50578.pdf