Samuel Delepoulle

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Growth chambers are used by agronomists for various experiments on plants. Because light impinging on plants is one of the main parameters of their growth, inhomogeneity in light reception can provide large bias in the experiments. In this paper we present the first steps of a framework which aims at computing the best locations of light sources that could(More)
Growth chambers are used by agronomists for various experiments on plants. Because light impinging on plants is one of the main parameters of their growth, inhomogeneity in light reception can provide large bias in the experiments. In this paper we present the first steps of a framework which aims at computing the best locations of light sources that could(More)
We study the evolution of social behaviors within a behav-ioral framework. To this end, we deene a \minimal social situation" that is experimented with both humans and simulations based on reinforcement learning algorithms. We analyse the dynamics of behaviors in this situation by way of operant conditioning. We show that the best reinforcement algorithm,(More)
Photorealistic lighting requires to use accurate rendering algorithms and realistic models of light sources. Photometric solids are provided by lighting designers in order to characterize the luminance distribution from real light sources. When accuracy is required the amount of discretized luminance directions and the number of photometric solids that have(More)
A no-reference image quality metric detecting both blur and noise is proposed in this paper. The proposed metric is based on IFS2 entropy applied on computer-generated images and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to(More)
This paper introduces a new algorithm, namely the Equi-Correlation Network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, by which we mean that ECON uses an $l_1$ regularization to produce sparse estimators, ECON efficiently rides the regularization path to obtain the estimator associated to any(More)