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David Rousseau
Researcher at University of Angers
Publications - 183
Citations - 2935
David Rousseau is an academic researcher from University of Angers. The author has contributed to research in topics: Stochastic resonance & Noise (signal processing). The author has an hindex of 24, co-authored 176 publications receiving 2374 citations. Previous affiliations of David Rousseau include University of Lyon & Institut national de la recherche agronomique.
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Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection
TL;DR: The value of various egocentric vision approaches in regard to performing joint acquisition and automatic image annotation rather than the conventional two-step process of acquisition followed by manual annotation is assessed.
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Assigning apples to individual trees in dense orchards using 3D colour point clouds
TL;DR: A 3D color point cloud processing pipeline to count apples on individual apple trees in trellis structured orchards and assign each apple to its bearing tree achieves an accuracy rate higher than 95%.
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Multichannel imaging for monitoring chemical composition and germination capacity of cowpea (Vigna unguiculata) seeds during development and maturation
Gamal ElMasry,Gamal ElMasry,Nasser S. Mandour,Yahya Ejeez,Didier Demilly,Salim S. Al-Rejaie,Jerome Verdier,Etienne Belin,Etienne Belin,David Rousseau,David Rousseau +10 more
TL;DR: The development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.
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On the value of CTIS imagery for neural-network-based classification: a simulation perspective.
TL;DR: It is shown that it is possible to learn information directly from the CTIS raw output, by training a neural network to perform binary classification on such images, which is the first application of compressed learning on a simulated CTIS system.
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Computer vision under inactinic light for hypocotyl-radicle separation with a generic gravitropism-based criterion
Landry Benoit,Etienne Belin,Carolyne Dürr,François Chapeau-Blondeau,Didier Demilly,Sylvie Ducournau,David Rousseau +6 more
TL;DR: An original protocol exploiting an inactinic green light, produced by a controlled LED source, coupled to a standard low-cost gray-level camera, connected to automation of image acquisition, can serve to improve high-throughput phenotyping equipments for analysis of seed quality and genetic variability.