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of 58
pro vyhledávání: '"Lauze, Francois"'
We present Locally Orderless Networks (LON) and its theoretic foundation which links it to Convolutional Neural Networks (CNN), to Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative
Externí odkaz:
http://arxiv.org/abs/2406.13514
Autor:
Brument, Baptiste, Bruneau, Robin, Quéau, Yvain, Mélou, Jean, Lauze, François Bernard, Jean-Denis, Durou, Jean-Denis, Calvet, Lilian
This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal, considering them
Externí odkaz:
http://arxiv.org/abs/2312.01215
Autor:
Netterstrøm, Rasmus, Kutuzov, Nikolay, Darkner, Sune, Pallesen, Maurits Jørring, Lauritzen, Martin Johannes, Erleben, Kenny, Lauze, Francois
Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a sca
Externí odkaz:
http://arxiv.org/abs/2303.16903
First Order Locally Orderless Registration (FLOR) is a scale-space framework for image density estimation used for defining image similarity, mainly for Image Registration. The Locally Orderless Registration framework was designed in principle to use
Externí odkaz:
http://arxiv.org/abs/2108.04926
We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers ``simple'' problems such as graph classification or regressing real-valued graph properties. For such tasks, the main require
Externí odkaz:
http://arxiv.org/abs/2106.03236
We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registrat
Externí odkaz:
http://arxiv.org/abs/1905.12056
Autor:
Lauze, Francois, Nielsen, Mads
We develop in this paper a generic Bayesian framework for the joint estimation of motion and recovery of missing data in a damaged video sequence. Using standard maximum a posteriori to variational formulation rationale, we derive generic minimum ene
Externí odkaz:
http://arxiv.org/abs/1809.07983
Autor:
Loog, Marco, Lauze, François
We start out by demonstrating that an elementary learning task, corresponding to the training of a single linear neuron in a convolutional neural network, can be solved for feature spaces of very high dimensionality. In a second step, acknowledging t
Externí odkaz:
http://arxiv.org/abs/1707.02813
We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be direct
Externí odkaz:
http://arxiv.org/abs/1707.01018
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