
F. Willot, H. Trumel, D. Jeulin (2019): The thermoelastic response of cracked polycrystals with hexagonal symmetry. Philosophical Magazine 99(5) 606—630.
The influence of a population of randomlyoriented cracks on the macroscopic thermal and linearelastic response of a hexagonal polycrystal is addressed using a selfconsistent method. Coupling between microcracks and crystal anisotropy is taken into account through the effective medium where all inhomogeneities are embedded. In the absence of cracks, the proposed approach reduces to the selfconsistent estimate of Berryman (2005). The accuracy of the present method is first assessed using numerical, Fourierbased computations. In the absence of crystal anisotropy, the estimates for the...
E.H. Diop, J. Angulo (2019): Levelings based on SpatiallyAdaptive ScaleSpaces using Local Image Features. IET Image Processing.
A. Borocco, B. Marcotegui (2019): Nonrigid shape registration using curvature information. 14th International Conference on Computer Vision Theory and Applications, Prague (Czech Republic).
This paper addresses a registration problem for an industrial control application: it meets the need to registrate a model on an image of a flexible object. We propose a nonrigid shape registration approach that deals with a great disparity of the number of points in the model and in the manufactured object. We have developed a method based on a classical minimization process combining a distance term and a regularization term. We observed that, even if the control points fall on the object boundary, the registration failed on high curvature points. In this paper we add a curvaturebased...
R. Rodriguez Salas, P. Dokládal, E. Dokladalova (2019): Rotationinvariant NN for learning naturally unoriented data.
Deep convolutional neural networks accuracy is heavily impacted by the rotations of the input data. In this paper, we propose a convolutional predictor that is invariant to rotations in the input. This architecture is capable of predicting the angular orientation without angleannotated data. Furthermore, the predictor maps continuously the random rotation of the input to a circular space of the prediction. For this purpose, we use the rototranslation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright...
R. Rodriguez Salas, E. Dokladalova, P. Dokládal (2019): Rotation invariant CNN using scattering transform for image classification.
Deep convolutional neural networks accuracy is heavily impacted by rotations of the input data. In this paper, we propose a convolutional predictor that is invariant to rotations in the input. This architecture is capable of predicting the angular orientation without angleannotated data. Furthermore, the predictor maps continuously the random rotation of the input to a circular space of the prediction. For this purpose, we use the rototranslation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright and...
P. CettourJanet, C. Cazorla, V. Machairas, Q. Delannoy, N. Bednarek, F. Rousseau, E. Decencière, N. Passat (2019): Watervoxels.
In this article, we present the $n$dimensional version of the waterpixels, namely the watervoxels. Waterpixels constitute a simple, yet efficient alternative to standard superpixel paradigms, initially developed in the field of computer vision for reducing the space cost of input images without altering the accuracy of further image processing / analysis procedures. Waterpixels were initially proposed in a 2dimensional version. Their extension to 3dimensionsand more generally $n$dimensionsis however possible, in particular in the Cartesian grid. Indeed, waterpixels mainly rely on a...
E. Bazan, P. Dokládal, E. Dokladalova (2019): Quantitative Analysis of Similarity Measures of Distributions.
The Earth Mover's Distance (EMD) is a metric based on the theory of optimal transport that has interesting geometrical properties for distributions comparison. However, the use of this measure is limited in comparison with other similarity measures as the KullbackLeibler divergence. The main reason was, until recently, the computation complexity. In this paper, we present a comparative study of the dissimilarity measures most used in the literature for the comparison of distributions through a colorbased image classification system and other simple examples with synthetic data. We show that...
List of all publications from the CMM, recorded on the HAL depository under the tag
ENSMP_CMM
.
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