
D. Jeulin (2019): Some dense random packings generated by the dead leaves model. Image Analysis and Stereology 38(1) 3.
The intact grains of the dead leaves model enables us to generate random media with non overlapping grains. Using the time non homogeneous sequential model with convex grains, theoretically very dense packings can be generated, up to a full covering of space. For these models, the theroretical volume fraction, the size distribution of grains, and the pair correlation function of centers of grains are given.
G. Franchi, E. Aldea, S. Dubuisson, I. Bloch (2019): Crowd Behavior Characterization for Scene Tracking. 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei (Taiwan) 1—8.
In this work, we perform an indepth analysis of the specific difficulties a crowded scene dataset raises for tracking algorithms. Starting from the standard characteristics depicting the crowd and their limitations, we introduce six entropy measures related to the motion patterns and to the appearance variability of the individuals forming the crowd, and one appearance measure based on Principal Component Analysis. The proposed measures are discussed on synthetic configurations and on multiple real datasets. These criteria are able to characterize the crowd behavior at a more detailed level...
T. Chabardes, P. Dokládal, M. Bilodeau (2019): A labeling algorithm based on a forest of decision trees. Journal of RealTime Image Processing.
Connected component labeling (CCL) is one of the most fundamental operations in image processing. CCL is a procedure for assigning a unique label to each connected component. It is a mandatory step between lowlevel and highlevel image processing. In this work, a general method is given to improve the neighbourhood exploration in a twoscan labeling. The neighbourhood values are considered as commands of a decision table. This decision table can be represented as a decision tree. A blockbased approach is proposed so that values of several pixels are given by one decision tree. This...
B. Figliuzzi (2019): Eikonalbased models of random tessellations. Image Analysis and Stereology 38(1) 15.
In this article, we propose a novel, efficient method for computing a random tessellation from its vectorial representation at each voxel of a discretized domain. This method is based upon the resolution of the Eikonal equation and has a complexity in O(N log N), N being the number of voxels used to discretize the domain. By contrast, evaluating the implicit functions of the vectorial representation at each voxel location has a complexity of O(N²) in the general case. The method also enables us to consider the generation of tessellations with rough interfaces between cells by simulating the...
J. Angulo (2019): Hierarchical Laplacian and Its Spectrum in Ultrametric Image Processing. International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing ISMM 2019, Saarbrücken (Germany) 11564 29—40.
The Laplacian of an image is one of the simplest and useful image processing tools which highlights regions of rapid intensity change and therefore it is applied for edge detection and contrast enhancement. This paper deals with the definition of the Laplacian operator on ultrametric spaces as well as its spectral representation in terms of the corresponding eigenfunctions and eigenvalues. The theory reviewed here provides the computational framework to process images or signals defined on a hierarchical representation associated to an ultrametric space. In particular, image regularization by...
J. Angulo (2019): Minkowski Sum of Ellipsoids and Means of Covariance Matrices. International Conference on Geometric Science of Information GSI 2019, Toulouse (France) 11712 107—115.
The Minkowski sum and difference of two ellipsoidal sets are in general not ellipsoidal. However, in many applications, it is required to compute the ellipsoidal set which approximates the Minkowski operations in a certain sense. In this study, an approach based on the socalled ellipsoidal calculus, which provides parameterized families of external and internal ellipsoids that tightly approximate the Minkowski sum and difference of ellipsoids, is considered. Approximations are tight along a direction l in the sense that the support functions on l of the ellipsoids are equal to the support...
A. Serna, B. Marcotegui, E. Decencière (2019): Segmenting junction regions without skeletonization using geodesic operators and the maxtree. 14th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, Saarbrücken (Germany).
In a 2D skeleton, a junction region indicates a connected component (CC) where a path splits into two or more different branches. In several real applications, structures of interest such as vessels, cables, fibers, wrinkles, etc. may be wider than one pixel. Since the user may be interested in the junction regions but not in the skeleton itself (e.g. in order to segment an object into single branches), it is reasonable to think about finding junction regions directly on objects avoiding skeletonization. In this paper we propose a solution to find junction regions directly on objects, which...
T. Asplund, A. Serna, B. Marcotegui, R. Strand, C. Luengo Hendriks (2019): Mathematical Morphology on Irregularly Sampled Data Applied to Segmentation of 3D Point Clouds of Urban Scenes. 14th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, Saarbrücken (Germany).
This paper proposes an extension of mathematical morphology on irregularly sampled signals to 3D point clouds. The proposed method is applied to the segmentation of urban scenes to show its applicability to the analysis of point cloud data. Applying the proposed operators has the desirable sideeffect of homogenizing signals that are sampled heterogeneously. In experiments we show that the proposed segmentation algorithm yields good results on the ParisrueMadame database and is robust in terms of sampling density, i.e. yielding similar labelings for more sparse samplings of the same scene.
W. Alves, C. Gobber, D. Da Silva, A. Morimitsu, R. Hashimoto, B. Marcotegui (2019): Ultimate levelings with strategy for filtering undesirable residues based on machine learning. 14th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, Saarbrücken (Germany).
Ultimate levelings are operators that extract important image contrast information from a scalespace based on levelings. During the residual extraction process, it is very common that some residues are selected from undesirable regions, but they should be filtered out. In order to avoid this problem some strategies can be used to filter residues extracted by ultimate levelings. In this paper, we introduce a novel strategy to filter undesirable residues from ultimate levelings based on a regression model that predicts the correspondence between objects of interest and residual regions. In...
A. Fehri, S. VelascoForero, F. Meyer (2019): Priorbased Hierarchical Segmentation Highlighting Structures of Interest. Mathematical Morphology  Theory and Applications 3 29 — 44.
Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest...
E. Decencière, A. Belhedi, S. Koudoro, F. Flament, G. François, V. Rubert, I. Pécile, J. Pierre (2019): A 2.5d approach to skin wrinkles segmentation. Image Analysis and Stereology 38(1) 75.
Wrinkles or creases are common structures on surfaces. Their detection is often challenging, and can be an important step for many different applications. For instance, skin wrinkle segmentation is a crucial step for quantifying changes in skin wrinkling and assessing the beneficial effects of dermatological and cosmetic antiageing treatments. A 2.5D approach is proposed in this paper to segment individual wrinkles on facial skin surface described by 3D point clouds. The method, based on mathematical morphology, only needs a few physical parameters as input, namely the maximum wrinkle width,...
B. Laÿ, R. Danno, G. Quellec, E. Decencière, A. Erginay, P. Massin, A. Le Guilcher, M. Lamard, B. Cochener, R. Alais (2019): RetinOpTICAutomatic Evaluation of Diabetic Retinopathy. ARVO, Vancouver (Canada).
Purpose: The RetinOpTIC project performs mass screening of color fundus images and assesses image quality and Diabetic Retinopathy (DR) grade. Algorithm performance is evaluated on the Messidor2 image database. Methods: Based on artificial intelligence (AI) solutions, referable DR is detected using convolutional neural networks (CNNs). The solution includes first the automatic assessment of the quality of the photography, and then the DR grade
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 LithiumIon Battery Positive Electrodes. Journal of The Electrochemical Society 166(8) A1692—A1703.
The electronic conductivity, at the multiscale, of lithiumion positive composite electrodes based on LiNi_{1/3}Mn_{1/3}Co_{1/3}O_2 and/or carboncoated LiFePO_4, carbon black and poly(vinylidene fluoride) mixture is modeled. The electrode microstructures are acquired numerically in 3D by Xray 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...
L. Lacourt, S. Forest, F. N'Guyen, D. Ryckelynck, F. Willot, S. Flouriot, V. De Rancourt, A. Thomas (2019): Étude numérique de la nocivité des défauts dans les soudures. AUSSSOIS2019 : Rupture des matériaux et structures – Mécanismes et modélisations face aux applications industrielles, Aussois (France).
L. Lacourt, S. Forest, D. Ryckelynck, F. Willot, S. Flouriot, V. De Rancourt (2019): Étude numérique de la nocivité des défauts dans les soudures. 14e Colloque National en Calcul des Structures, Presqu’île de Giens (France).
F. Rabette, H. Trumel, F. Willot (2019): Modélisation multiéchelle par champ de phase de la microfissuration d'un polycristal organique de forte anisotropie cristalline par FFT. 14e Colloque National en Calcul des Structures, Presqu’île de Giens (France).
H. Trumel, F. Rabette, F. Willot, R. Brenner, E. Ongari, M. Biessy, D. Picart (2019): Understanding the thermomechanical behavior of a TATBbased explosive via microstructurelevel simulations. Part I: Microcracking and viscoelasticity. Europyro 44th International Pyrotechnics Seminar, Tours (France).
In view of a better understanding of the thermomechanical behavior of pressed explosives, a Fourierbased computational tool is used to perform numerical homogenization and compare predictions to experimental macroscopic properties. This is first done in a purely thermoelastic context on simplified polycrystalline virtual microstructures, then extended to cracked polycrystalline ones. A further extension is proposed, aiming at predicting the nucleation and propagation of (micro)cracks. Besides, a meanfield (selfconsistent) approach is also followed, providing accurate thermoelastic...
F. Rabette, F. Willot, H. Trumel (2019): Homogénéisation en champs complets par FFT pour un matériau énergétique à forte anisotropie cristalline : prise en compte de la microfissuration par une méthode de champ de phase. Colloque National MECAMAT 2019: Rupture des Matériaux et des Structures, Aussois (France).
H. Thomas, C.R. Qi, J.E. Deschaud, B. Marcotegui, F. Goulette, L.J. Guibas (2019): KPConv: Flexible and Deformable Convolution for Point Clouds. The IEEE International Conference on Computer Vision (ICCV), Séoul (South Korea).
We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of kernel points gives KPConv more flexibility than fixed grid convolutions. Furthermore, these locations are continuous in space and can be learned by the network. Therefore, KPConv can be extended to deformable convolutions that learn to adapt kernel points to local geometry....
D. DuqueArias, S. VelascoForero, F. Goulette, J.E. Deschaud, B. Marcotegui (2019): A graphbased 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 graphbased formulation of the wellknown Color Lines model. It generalizes the lines to piecewise 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...
E. Bazan, P. Dokládal, E. Dokladalova (2019): Quantitative Analysis of Similarity Measures of Distributions. British Machine Vision Conference (BMVC), Cardiff (United Kingdom).
There are many measures of dissimilarity that, depending on the application, do not always have optimal behavior. In this paper, we present a qualitative analysis of the similarity measures most used in the literature and the Earth Mover's Distance (EMD). The EMD is a metric based on the theory of optimal transport with interesting geometrical properties for the comparison of distributions. However, the use of this measure is limited in comparison with other similarity measures. The main reason was, until recently, the computational complexity. We show the superiority of the EMD through three...
S. VelascoForero, B. Ponchon, S. Blusseau, J. Angulo, I. Bloch (2019): On approximating mathematical morphology operators via deep learning techniques. 15th International Congress for Stereology and Image Analysis, Aarhus (Denmark) 51.
Mathematical Morphology (MM) is a wellestablished discipline whose aim is mainly to provide tools to characterise complex object via their shape/size features. This study addresses the problem of robust approximation of mathematical morphology (MM) operators by deep learning methods. We present two cases, (a) Asymmetric autoencoders for partbased approximations of classical MM in the sense of [1] and, (b) imagetoimage translation networks [2] to produce robust MM operators in presence of noise.
S. Blusseau, Y. Zhang, S. VelascoForero, I. Bloch, J. Angulo (2019): Pruning neural networks thanks to morphological layers. 15th International Congress for Stereology and Image Analysis, Aarhus (Denmark) 17.
Motivated by recent advances in morphological neural networks, we further study the properties of morphological units when incorporated in layers of conventional neural networks. We confirm and extend the observation that a Maxplus layer can be used to select relevant filters and reduce redundancy in its previous layer, without incurring performance loss. We present several experiments in image processing, showing that this filter selection property seems efficient and robust. We also point out the close connection between Maxout networks and our pruned Maxplus networks. The code related to...
Y. Yan, P.H. Conze, E. Decencière, M. Lamard, G. Quellec, B. Cochener, G. Coatrieux (2019): Cascaded multiscale convolutional encoderdecoders for breast mass segmentation in highresolution mammograms. IEEE International Engineering in Medicine and Biology Conference, Berlin (Germany).
Y. Xiao, E. Decencière, S. VelascoForero, 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 colorstained 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 onthefly...
J. Chaniot, M. Moreaud, L. Sorbier, T. Fournel, J.M. Becker (2019): Tortuosimetric operator for complex porous media characterization. Image Analysis and Stereology 38(1) 25—41.
Geometric tortuosity is one of the foremost topological characteristics of porous media. Despite the various definitions in the literature, to our knowledge, they are all linked to an arbitrary propagation direction. This paper proposes a novel topological descriptor, named Mtortuosity, by giving a more straightforward definition, describing the data regardless of physicochemical processes. Mtortuosity, based on the concept of geometric tortuosity, is a scalable descriptor, meaning that information of several dimensions (scalar, histograms, 3D maps) is available. It is applicable on complex...
R. Rodriguez Salas, E. Dokladalova, P. Dokládal (2019): Rotation invariant CNN using scattering transform for image classification. IEEE International Conference on Image Processing (ICIP), 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 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. Image Processing On Line 9 317—328.
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...
Kaeshammer, P. Dokládal, F. Willot, B. Erzar, S. Belon, L. Borne (2019): A Morphological Study of Energetic Materials: Analysis of Microcomputed 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 Microcomputed Tomography Images Analysis. 50th International Annual Conference of the Fraunhofer ICT, Karlsruhe (Germany).
Impact experiments were performed at the frenchgerman research Institute of SaintLouis 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 microcomputed tomography (µCT)...
M. Neumann, B. Abdallah, L. Holzer, F. Willot, V. Schmidt (2019): Stochastic 3D Modeling of ThreePhase 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 graphbased model and a pluriGaussian model, that have been introduced to model the transport properties of threephase 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 microstructure characteristics. In particular, an analytical expression is obtained for the expected length of triple phase boundary per unit volume in the pluriGaussian 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. VelascoForero, S. Blusseau, J. Angulo, I. Bloch (2019): Partbased approximations for morphological operators using asymmetric autoencoders. International Symposium on Mathematical Morphology, Saarbrücken (Germany).
This paper addresses the issue of building a partbased representation of a dataset of images. More precisely, we look for a nonnegative, 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, nonnegative autoencoder where the encoder is deep (for accuracy) and the decoder shallow (for interpretability). This method compares favorably to the...
Y. Zhang, S. Blusseau, S. VelascoForero, I. Bloch, J. Angulo (2019): Maxplus 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 Maxplus 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 Maxplus 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 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 13(10) 1597—1607.
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.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. HuertasCompany, 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. VelascoForero, G.A. Verdoes Kleijn, G. Vernardos (2019): The Strong Gravitational Lens Finding Challenge. Astron.Astrophys. 625 A119.
Largescale imaging surveys will increase the number of galaxyscale 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...
K. Chang (2019): Machine learning for image segmentation.
In this PhD thesis, our aim is to establish a general methodology for performing the segmentation of a dataset constituted of similar images with only a few annotated images as training examples. This methodology is directly intended to be applied to images gathered in Earth observation or materials science applications, for which there is not enough annotated examples to train stateoftheart deep learning based segmentation algorithms. The proposed methodology starts from a superpixel partition of the image and gradually merges the initial regions until anactual segmentation is obtained....
F. Willot (2019): Localization in random media and its effect on the homogenized behavior of materials.
The present manuscript is submitted in partial fulfillment of my application to the degree of ``Habilitation à diriger des recherches'' at Sorbonne University. Its main contribution is a study in theoretical mechanics devoted to homogenization problems in the context of degenerate (nonstrictly convex) local response of one of the phases, which can serve as idealized models for porous or rigidlyreinforced materials exhibiting perfectlyplastic behavior. In these situations plastic flow preferentially concentrates along shear bands; as a result the material effective response is governed by...
S. Blusseau (2019): Mathematical morphology in nonEuclidean spaces and medical images Technical report.
K. Chang, B. Figliuzzi (2019): Fast Marching Based Superpixels Generation. Burgeth B., Kleefeld A., Naegel B., Passat N., Perret B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science, vol 11564. Springer, Cham 350—361.
R. Rodriguez Salas, P. Dokládal, E. Dokladalova (2019): REDNN: RotationEquivariant 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 rototranslational 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 selforganization capability and learns to predict the class and the rotation angle...
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. BonodBidaud, P. Dokládal, F. Ruggiero, J.M. Allain, M.C. SchanneKlein (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): 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...
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
.
See also: