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Mise en œuvre opérationnelle de l’outil FlowCAM / ZooPhytoImage dans le cadre de la surveillance REPHY. Action 9 - FlowCAM / ZooPhytoImage - Livrable 2. Rapport final, mars 2015. ArchiMer
Neaud-masson, Nadine; Lefebvre, Alain; Belin, Catherine; Soudant, Dominique.
This deliverable contains the work that was carried out in 2014 for the FlowCAM / ZooPhytoImage tool actually becomes a usable operational tool in the REPHY, and as such, can provide results supplying the assessments of WFD phytoplankton indicator. This deliverable is complementary to the deliverable 1, provided for the same action, entitled « Evolutive version of the operational tool for digitization and semi-automated analysis of phytoplankton images, using the FlowCAM device and the Zoo/PhytoImage software. New perspectives » This deliverable has five parts whose content is summarized below. 1. Work done for operational implementation in 2014 under the REPHY. These tasks affect either the scanning operations of the phytoplankton images by FlowCAM, or...
Tipo: Text Palavras-chave: FlowCAM; ZooPhytoImage; REPHY; Observation phytoplancton; Set d’apprentissage; Littoral français.; FlowCAM; ZooPhytoImage; REPHY; Phytoplankton observation; Training set; French coast..
Ano: 2015 URL: http://archimer.ifremer.fr/doc/00361/47262/47227.pdf
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Protocole sur les outils de reconnaissance optimisés Manche Atlantique. Action 9 - FlowCam ZooPhytoImage. Livrable n° 2. Rapport final ArchiMer
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
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Optimisation de l’identification et du dénombrement du microphytoplancton avec le système couplé de numérisation et d’analyse d’images FlowCAM / ZooPhytoImage (système innovant). Action 9 – Livrable 2. Mise en oeuvre opérationnelle de l’outil FlowCAM-Zoo/PhytoImage dans le cadre de la surveillance REPHY. Résultats sur 12- 18 mois. Rapport final, février 2016 ArchiMer
Neaud-masson, Nadine; Lefebvre, Alain; Belin, Catherine; Gauthier, Emilie; Huguet, Antoine; Szidon, Antoine; Lefebvre, Arnaud; Cuvelliez, Rémi.
This deliverable includes several documents that reflect the operational implementation of the tool FlowCAM / ZooPhytoImage in the framework of REPHY. Among these documents, three reports make an assessment of the entire year 2015, which is not the case with other documents (Master 2 report issued in spring 2015, and minutes of meetings over the year). This summary will be limited to the findings of the three reports providing an updated view of the implementation of the tool. Work completed in 2015 for the operational implementation. The many scans performed in 2015 with the three FlowCAM present at Ifremer (Boulogne‐sur‐Mer, Arcachon and Nantes) allowed, one hand to feed the training sets of the tool FlowCAMZoo/PhytoImage, the other test its routine use....
Tipo: Text Palavras-chave: Phytoplancton; FlowCAM; Zoo/PhytoImage; Numérisation; Analyse d’images; Set d’apprentissage; Outil de reconnaissance; Architecture de données; Bancarisation; Quadrige; Phytoplankton; FlowCAM; Zoo/PhytoImage; Scanning; Image analysis; Training set; Recognition tool; Data architecture; Banking; Quadrige.
Ano: 2016 URL: http://archimer.ifremer.fr/doc/00363/47413/47409.pdf
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