References of "Dumont, Marie"
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See detailNon-visual effect of light on cognitive brain function: Impact of lens yellowing in aging
Daneault, Véronique; Dumont, Marie; Massé, Eric et al

Poster (2017, February 10)

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See detailLight-sensitive brain pathways and aging
Daneault, Véronique; Dumont, Marie; Massé, Eric et al

in Journal of Physiological Anthropology (2016), 35(9),

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See detailAging Reduces the Stimulating Effect of Blue Light on Cognitive Brain Functions
Daneault, Véronique; Hébert, Marc; Albouy, Geneviève et al

in Sleep (2014)

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See detailBlue Light Stimulates Cognitive Brain Activity in Visually Blind Individuals
Vandewalle, Gilles ULiege; Collignon, Olivier; Hull, Joseph et al

in Journal of Cognitive Neuroscience (2013)

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See detailAge-Related Changes in Circadian Rhythms During Adulthood
Daneault, Véronique; Vandewalle, Gilles ULiege; Najjar, Raimond P. et al

in The Encyclopedia of Sleep (2013)

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See detailDoes pupil constriction under blue and green monochromatic light exposure change with age?
Daneault, Véronique; Vandewalle, Gilles ULiege; Hébert, Marc et al

in Journal of Biological Rhythms (2012), 27(3), 257-264

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See detailFast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees
Dumont, Marie; Marée, Raphaël ULiege; Wehenkel, Louis ULiege et al

in Proc. International Conference on Computer Vision Theory and Applications (VISAPP) (2009, February)

This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of ... [more ▼]

This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter. [less ▲]

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See detailRandom Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation
Marée, Raphaël ULiege; Dumont, Marie; Geurts, Pierre ULiege et al

Poster (2007, July 22)

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See detailRandom Subwindows and Multiple Output Decision Trees for Generic Image Annotation
Dumont, Marie; Marée, Raphaël ULiege; Geurts, Pierre ULiege et al

Poster (2007)

Detailed reference viewed: 77 (8 ULiège)