Publications from the Center of Mathematical Morphology

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F. Cadiou, A. Etiemble, T. Douillard, F. Willot, O. Valentin, J.C. Badot, B. Lestriez, E. Maire (2019): Numerical Prediction of Multiscale Electronic Conductivity of Lithium-Ion Battery Positive Electrodes. Journal of The Electrochemical Society 166(8) A1692—A1703.
The electronic conductivity, at the multiscale, of lithium-ion positive composite electrodes based on LiNi_{1/3}Mn_{1/3}Co_{1/3}O_2 and/or carbon-coated LiFePO_4, carbon black and poly(vinylidene fluoride) mixture is modeled. The electrode microstructures are acquired numerically in 3D by X-ray tomography and FIB/SEM nanotomography and numerically segmented to perform electrostatic simulations using Fast Fourier Transform (FFT) method. Such simulations are easy and quick to perform because they are directly computed on the grid represented by the voxels in the 3D volumes. Numerical results...

M. Neumann, B. Abdallah, L. Holzer, F. Willot, V. Schmidt (2019): Stochastic 3D Modeling of Three-Phase Microstructures for Predicting Transport Properties: A Case Study. Transport in Porous Media 128(1) 179—200.
We compare two conceptually different stochastic microstructure models , i.e., a graph-based model and a pluri-Gaussian model, that have been introduced to model the transport properties of three-phase microstructures occurring, e.g., in solid oxide fuel cell electrodes. Besides comparing both models, we present new results regarding the relationship between model parameters and certain mi-crostructure characteristics. In particular, an analytical expression is obtained for the expected length of triple phase boundary per unit volume in the pluri-Gaussian model. As a case study, we consider...

J. Serra, F. Willot (2019): Special topic on multiscale modeling of granular media: a tribute to Prof. Dominique Jeulin. Image Analysis and Stereology 38(1) 1.
A few words on the present special topic, devoted to the multiscale modeling of granular media, and published in honor of Prof. Dominique Jeulin's enduring contribution to the wide field of image analysis, random structures and material science.

B. Ponchon, S. Velasco-Forero, S. Blusseau, J. Angulo, I. Bloch (2019): Part-based approximations for morphological operators using asymmetric auto-encoders. International Symposium on Mathematical Morphology, Saarbrücken (Germany).
This paper addresses the issue of building a part-based representation of a dataset of images. More precisely, we look for a non-negative, sparse decomposition of the images on a reduced set of atoms, in order to unveil a morphological and interpretable structure of the data. Additionally, we want this decomposition to be computed online for any new sample that is not part of the initial dataset. Therefore, our solution relies on a sparse, non-negative auto-encoder where the encoder is deep (for accuracy) and the decoder shallow (for interpretability). This method compares favorably to the...

Y. Zhang, S. Blusseau, S. Velasco-Forero, I. Bloch, J. Angulo (2019): Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks. International Symposium on Mathematical Morphology, Saarbrücken (Germany).
Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks. Besides showing that they can be easily implemented with modern machine learning frameworks , we confirm and extend the observation that a Max-plus layer can be used to select important filters and reduce redundancy in its previous layer, without incurring performance loss. Experimental results demonstrate that the filter selection strategy enabled by...

R. Rodriguez Salas, R. Rodriguez, E. Dokladalova, P. Dokládal (2019): Rotation invariant CNN using scattering transform for image classification. ICIP 2019 proceedings, Taipei (Taiwan).
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 angle-annotated 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 roto-translation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright and...

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 randomly-oriented cracks on the macroscopic thermal and linear-elastic response of a hexagonal polycrystal is addressed using a self-consistent method. Coupling between micro-cracks 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 self-consistent estimate of Berryman (2005). The accuracy of the present method is first assessed using numerical, Fourier-based computations. In the absence of crystal anisotropy, the estimates for the...

E.H. Diop, J. Angulo (2019): Levelings based on Spatially-Adaptive Scale-Spaces using Local Image Features. IET Image Processing.

A. Borocco, B. Marcotegui (2019): Non-rigid 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 non-rigid 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 curvature-based...

J. DIRRENBERGER, S. FOREST, D. Jeulin (2019): Computational Homogenization of Architectured Materials. Architectured Materials in Nature and Engineering 89—139.

S. Bancelin, B. Lynch, C. Bonod-Bidaud, P. Dokládal, F. Ruggiero, J.M. Allain, M.C. Schanne-Klein (2019): Combination of Traction Assays and Multiphoton Imaging to Quantify Skin Biomechanics. Collagen : Methods and Protocols 1944 145—155.
An important issue in tissue biomechanics is to decipher the relationship between the mechanical behavior at macroscopic scale and the organization of the collagen fiber network at microscopic scale. Here, we present a protocol to combine traction assays with multiphoton microscopy in ex vivo murine skin. This multiscale approach provides simultaneously the stress/stretch response of a skin biopsy and the collagen reorganization in the dermis by use of second harmonic generation (SHG) signals and appropriate image processing.

R. Rodriguez Salas, P. Dokládal, E. Dokladalova (2019): Rotation-invariant NN for learning naturally un-oriented 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 angle-annotated 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 roto-translation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright...

P. Cettour-Janet, 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 2-dimensional version. Their extension to 3-dimensions---and more generally $n$-dimensions---is 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 Kullback-Leibler 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 color-based 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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