References of "Stevens, Benjamin"
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See detailA hybrid human-computer approach for large-scale image-based measurements using web services and machine learning
Marée, Raphaël ULg; Rollus, Loïc ULg; Stevens, Benjamin ULg et al

in Proceedings IEEE International Symposium on Biomedical Imaging (2014, May)

We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale ... [more ▼]

We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale imaging data. We describe our main methodological choices, and then illustrate the benefits of the approach (workload reduction, improved precision, scalability, and traceability) on hundreds of whole-slide images of biological tissue slices in cancer research. [less ▲]

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See detailA rich internet application for remote visualization and collaborative annotation of digital slide images in histology and cytology
Marée, Raphaël ULg; Stevens, Benjamin ULg; Rollus, Loïc ULg et al

in Diagnostic Pathology (2013), 8(S1), 26

This work proposes a new web-based tool to ease collaborative projects in digital histology and cytology.

Detailed reference viewed: 107 (38 ULg)
See detailProgramme Wist2 Auralias. Rapport scientifique final Lucid-ULg
Leclercq, Pierre ULg; Schmitz, Dimitri ULg; Dawans, Arnaud et al

Report (2011)

Immersion audio-visuelle interactive dédiée aux applications en acoustique architecturale et s’appuyant sur le rendu sonore à trois dimensions.

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See detailAuralias
Leclercq, Pierre ULg; Stevens, Benjamin ULg; Demaret, Jean-Noël ULg et al

Software (2010)

Detailed reference viewed: 53 (20 ULg)
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See detailA Machine Learning Approach for Material Detection in Hyperspectral Images
Marée, Raphaël ULg; Stevens, Benjamin ULg; Geurts, Pierre ULg et al

in Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09) (2009)

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes ... [more ▼]

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes and by using extremely randomized trees with multiple outputs as a classifier. Promising results are shown on a dataset of more than 60 hypercubes. [less ▲]

Detailed reference viewed: 53 (14 ULg)