Publications from the Center of Mathematical Morphology

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D. Duque-Arias, S. Velasco-Forero, J.E. Deschaud, F. Goulette, B. Marcotegui (2019): A graph-based color lines model for image analysis. International Conference on Image Analysis and Processing, Trento (Italy).
This paper addresses the problem of obtaining a concise description of spectral representation for color images. The proposed method is a graph-based formulation of the well-known Color Lines model. It generalizes the lines to piece-wise lines, been able to fit more complex structures. We illustrate the goodness of proposed method by measuring the quality of the simplified representations in images and videos. The quality of video sequences reconstructed by means of proposed color lines extracted from the first frame demonstrates the robustness of our representation. Our formalism allows to...

Y. Yan, P.H. Conze, E. Decencière, M. Lamard, G. Quellec, B. Cochener, G. Coatrieux (2019): Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms. IEEE International Engineering in Medicine and Biology Conference, Berlin (Germany).

Y. Xiao, E. Decencière, S. Velasco-Forero, H. Burdin, T. Bornschlögl, F. Bernerd, E. Warrick, T. Baldeweck (2019): A NEW COLOR AUGMENTATION METHOD FOR DEEP LEARNING SEGMENTATION OF HISTOLOGICAL IMAGES. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), Venise (France).
This paper addresses the problem of labeled data insufficiency in neural network training for semantic segmentation of color-stained histological images acquired via Whole Slide Imaging. It proposes an efficient image augmentation method to alleviate the demand for a large amount of labeled data and improve the network's generalization capacity. Typical image augmentation in bioimaging involves geometric transformation. Here, we propose a new image augmentation technique by combining the structure of one image with the color appearance of another image to construct augmented images on-the-fly...

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...

P. Cettour-Janet, C. Cazorla, V. Machairas, Q. Delannoy, N. Bednarek, F. Rousseau, E. Decencière, N. Passat (2019): Watervoxels. Image Processing On Line.
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...

Kaeshammer, P. Dokládal, F. Willot, B. Erzar, S. Belon, L. Borne (2019): A Morphological Study of Energetic Materials: Analysis of Micro-computed Tomography Images to Generate Representative Microstructures. Europyro 2019 44th International Pyrotechnics Seminar, Tours (France).
In previous work, impact experiments were performed at ISL on three energetic materials composed of 70% in weight of RDX particles embedded in a wax matrix. These materials differ by the microstructural properties of the explosive particles. The experimental results reveal that the detonation thresholds, and so the sensitivity to shock, are different for each sample. To better understand these results, we characterize the microstructural properties of these compositions and generate virtual microstructures representative of the real microstructures. First, the microstructures of the three...

Kaeshammer, P. Dokladal, F. Willot, S. Belon, L. Borne (2019): Generation of Virtual Microstructures of Energetic Materials Based on Micro-computed Tomography Images Analysis. 50th International Annual Conference of the Fraunhofer ICT, Karlsruhe (Germany).
Impact experiments were performed at the french-german research Institute of Saint-Louis on three energetic materials composed of 70 % in weight of RDX particles embedded in a wax matrix. These materials differ by the microstructural properties of the explosive particles. The experimental results reveal that the detonation thresholds, and so the sensitivity to shock, are different for each sample. To better understand these results, we characterize the microstructural properties of these compositions. The microstructures of the three materials are imaged with micro-computed tomography (µCT)...

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...

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...

R.B. Metcalf, M. Meneghetti, C. Avestruz, F. Bellagamba, C.R. Bom, E. Bertin, R. Cabanac, F. Courbin, A. Davies, E. Decencière, R. Flamary, R. Gavazzi, M. Geiger, P. Hartley, M. Huertas-Company, N. Jackson, C. Jacobs, E. Jullo, J.P. Kneib, L.V.E. Koopmans, F. Lanusse, C.L. Li, Q. Ma, M. Makler, N. Li, M. Lightman, C.E. Petrillo, S. Serjeant, C. Schäfer, A. Sonnenfeld, A. Tagore, C. Tortora, D. Tuccillo, M.B. Valentín, S. Velasco-Forero, G.A. Verdoes Kleijn, G. Vernardos (2019): The Strong Gravitational Lens Finding Challenge. Astron.Astrophys. 625 A119.
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open...

R. Rodriguez Salas, P. Dokládal, E. Dokladalova (2019): RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation.
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated objects. This architecture is built around an ordered ensemble of oriented edge detectors to create a roto-translational space that transforms the input rotation into translation. This space allows the subsequent predictor to learn the internal spatial and angular relations of the objects regardless of their orientation. No data augmentation is needed and the model remains significantly smaller. It presents a self-organization capability and learns to predict the class and the rotation angle...

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 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...

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...

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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