Publications from the Center of Mathematical Morphology

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E.H.S. Diop, J. Angulo (2020): Inhomogeneous morphological PDEs for robust and adaptive image shock filters. IET Image Processing 14(6) 1035—1046.
Classical morphological filters suffer from well performing in a noisy environment, and intrinsic image structures are not taken into account. The authors propose here an alternative to overcome such weaknesses, by properly using robust shock filters and inhomogeneity. Thus, they obtain multiscale morphological operators by using image edge functions as local weights in inhomogeneous Hamiltonians in classical multiscale dilations/erosions formulated with partial differential equations (PDEs). They provide the equivalent sup–inf-based formulations, and derive sharpening/enhancement methods....

F. Cadiou, T. Douillard, F. Willot, J.C. Badot, B. Lestriez, E. Maire (2020): Effective Electronic and Ionic Conductivities of Dense EV-Designed NMC-Based Positive Electrodes using Fourier Based Numerical Simulations on FIB/SEM Volumes. Journal of The Electrochemical Society 167(14) 140504.
Experimental conductivity measurements, obtained on NMC 532 -based electrodes with markedly different porosities and made with percolating and non-percolating CB/PVdF phase, are compared with full-field numerical predictions. These ones are based on segmented nanotomography images and phase bulk properties and contain no tunable parameter. A good agreement between the calculated and measured transport properties is observed. 3D current density fields give insights on the microstructure impacts on the current density distribution. Ionic transport is dominated by low tortuosity micrometric...

K. Chang, B. Figliuzzi (2020): Fast marching based superpixels. Mathematical Morphology - Theory and Applications 4(1) 127—142.
In this article, we present a fast-marching based algorithm for generating superpixel (FMS) partitions of images. The idea behind the algorithm is to draw an analogy between waves propagating in an heterogeneous medium and regions growing on an image at a rate depending on the local color and texture. The FMS algorithm is evaluated on the Berkeley Segmentation Dataset 500. It yields results in term of boundary adherence that are comparable to the ones obtained with state of the art algorithms including SLIC or ERGC, while improving on these algorithms in terms of compactness and density.

F. Willot (2020): The effective conductivity of strongly nonlinear media: The dilute limit. International Journal of Solids and Structures 184 287—295.
This work is a combined numerical and analytical investigation of the effective conductivity of strongly nonlinear media in two dimensions. The nonlinear behavior is characterized by a threshold value for the maximal absolute current. Our main focus is on random media containing an infinitesimal proportion f≪1 of insulating phase. We first consider a random conducting network on a square grid and establish a relationship between the length of minimal paths spanning the network and the network's effective response. In the dilute limit f≪1, the network's effective conductivity scales, to...

M. Neumann, O. Stenzel, F. Willot, L. Holzer, V. Schmidt (2020): Quantifying the influence of microstructure on effective conductivity and permeability: Virtual materials testing. International Journal of Solids and Structures 184 211—220.
Effective conductivity and permeability of a versatile, graph-based model of random structures are investigated numerically. This model, originally introduced in Gaiselmann et al. (2014) allows one to simulate a wide class of realistic materials. In the present work, an extensive dataset of two-phase microstructures with wide-ranging morphological features is used to assess the relationship between microstructure and effective transport properties, which are computed using Fourier-based methods on digital images. Our main morphological descriptors are phase volume fractions, mean geodesic...

V. Bortolussi, B. Figliuzzi, F. Willot, M. Faessel, M. Jeandin (2020): Electrical Conductivity of Metal–Polymer Cold Spray Composite Coatings onto Carbon Fiber-Reinforced Polymer. Journal of Thermal Spray Technology 29(4) 642—656.
Cold spray is a promising process to coat polymers and carbon fiber-reinforced polymer (CFRP). The choice of the metal-polymer couple of materials, however, has a strong influence on coating build-up and properties. In the present work, we show that spraying mixtures of copper and polymer particles lead to composite coating. We observe that the polymer promotes coating build-up onto CFRP to the expense of the electrical conductivity of the coating as a result of its insulating properties. The present work investigates the influence of the coating microstructure on electrical conductivity....

M. Bauw, S. Velasco-Forero, J. Angulo, C. Adnet, O. Airiau (2020): From unsupervised to semi-supervised anomaly detection methods for HRRP targets. 2020 IEEE Radar Conference (RadarConf20), Florence (Italy) 1—6.
Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature. This work aims at presenting a benchmark gathering common and recently introduced unsupervised anomaly detection (AD) methods, the results being generated using high-resolution range profiles. A semi-supervised AD (SAD) is considered to demonstrate the added value of having a few labeled anomalies to improve performances. Experiments were conducted with and without pollution of the training set with anomalous samples...

A. HAMMOUMI, M. Moreaud, C. Ducottet, S. Desroziers (2020): Adding geodesic information and stochastic patch-wise image prediction for small dataset learning. Neurocomputing.
Most recent methods of image augmentation and prediction are building upon the deep learning paradigm. A careful preparation of the image dataset and the choice of a suitable network architecture are crucial steps to assess the desired image features and, thence, achieve accurate predictions. We first propose to help the learning process by adding structural information with specific distance transform to the input image data. To handle cases with limited number of training samples, we propose a patch-based procedure with a stratified sampling method at inference. We validate our approaches...

E. Tancrède‐Bohin, T. Baldeweck, S. Brizion, E. Decencière, S. Victorin, B. Ngo, E. Raynaud, L. Souverain, M. Bagot, A.M. Pena (2020): In vivo multiphoton imaging for non‐invasive time course assessment of retinoids effects on human skin. Skin Research and Technology 26(6) 794—803.
Background In vivo multiphoton imaging and automatic 3D image processing tools provide quantitative information on human skin constituents. These multiphoton‐based tools allowed evidencing retinoids epidermal effects in the occlusive patch test protocol developed for antiaging products screening. This study aimed at investigating their relevance for non‐invasive, time course assessment of retinoids cutaneous effects under real‐life conditions for one year. Materials and Methods Thirty women, 55‐65 y, applied either retinol (RO 0.3%) or retinoic acid (RA 0.025%) on one forearm dorsal...

F. Willot, R. Brenner, H. Trumel (2020): Elastostatic field distributions in polycrystals and cracked media. Philosophical Magazine 100(6) 661—687.
This work addresses the problem of the reconstruction of the local fields distribution occurring in heterogeneous linear elastic solids. The constitutive heterogeneities are crystals and cracks. Through comparisons with FFT computations, it is shown that self-consistent estimates together with an assumption of normal distribution at the phase scale provide an accurate description of the elastostatic field histograms in polycrystals without cracks. In the case of inter and transgranular cracks, full-field FFT simulations indicate that the field histograms present van Hove singularities. Their...

T. Ku, R. Veltkamp, B. Boom, D. Duque-Arias, S. Velasco-Forero, J.E. Deschaud, F. Goulette, B. Marcotegui, S. Ortega, A. Trujillo, J. Pablo Suárez, J. Santana, C. Ramírez, K. Akadas, S. Gangisetty (2020): SHREC 2020 Track: 3D Point Cloud Semantic Segmentation for Street Scenes. Computers and Graphics 93 13—24.
Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, it's cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with...

J. Chaniot, M. Moreaud, L. Sorbier, D. Jeulin, J.M. Becker, T. Fournel (2020): Heterogeneity assessment based on average variations of morphological tortuosity for complex porous structures characterization. Image Analysis and Stereology 39(2) 111—128.
Morphological characterization of porous media is of paramount interest, mainly due to the connections between their physicochemical properties and their porous microstructure geometry. Heterogeneity can be seen as a geometric characteristic of porous microstructures. In this paper, two novel topological descriptors are proposed, based on the M-tortuosity formalism. Using the concept of geometric tortuosity or morphological tortuosity, a first operator is defined, the H-tortuosity. It estimates the average variations of the morphological tortuosity as a function of the scale, based on Monte...

E. Thompson, S. Biasotti, A. Giachetti, C. Tortorici, N. Werghi, A. Obeid, S. Berretti, H.P. Nguyen-Dinh, M.Q. Le, H.D. Nguyen, M.T. Tran, L. Gigli, S. Velasco-Forero, B. Marcotegui, I. Sipiran, B. Bustos, I. Romanelis, V. Fotis, G. Arvanitis, K. Moustakas, E. Otu, R. Zwiggelaar, D. Hunter, Y. Liu, Y. Arteaga, R. Luxman (2020): SHREC'20 track: Retrieval of digital surfaces with similar geometric reliefs. Computers and Graphics 91 199—218.
This paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and 1 the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge,...

S. Blusseau, B. Ponchon, S. Velasco-Forero, J. Angulo, I. Bloch (2020): Approximating morphological operators with part-based representations learned by asymmetric auto-encoders. Mathematical Morphology - Theory and Applications 4(1) 64 — 86.
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 explainable 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 explainability). This method compares favorably to the...

L. Gigli, B.R. Kiran, T. Paul, A. Serna, N. Vemuri, B. Marcotegui, S. Velasco-Forero (2020): Road Segmentation on low resolution Lidar point clouds for autonomous vehicles. XXIV International Society for Photogrammetry and Remote Sensing Congress, Nice (France).
Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsampling image based representations of dense point clouds on the accuracy of the road segmentation task. In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map...

L. Gigli, S. Velasco-Forero, B. Marcotegui (2020): On minimum spanning tree streaming for hierarchical segmentation. Pattern Recognition Letters 138 155—162.
The minimum spanning tree (MST) is one the most popular data structure used to extract hierarchical information from images. This work addresses MST construction in streaming for images. First, we focus on the problem of computing a MST of the union of two graphs with a non-empty intersection. Then we show how our solution can be applied to streaming images. The proposed solution relies on the decomposition of the data in two parts. One stable that does not change in the future. This can be stocked or used for further treatments. The other unstable needs further information before becoming...

A.T. Fialho Batista, W. Baaziz, A.L. Taleb, J. Chaniot, M. Moreaud, C. Legens, A. Aguilar-Tapia, O. Proux, J.L. Hazemann, F. Diehl, C. Chizallet, A.S. Gay, O. Ersen, P. Raybaud (2020): Atomic scale insight into the formation, size and location of plati- num nanoparticles supported on γ-alumina. ACS Catalysis 10(7) 4193—4204.
The clear description of the morphology and location, with respect to the support, of metallic sub-nanometric particles remains a current experimental strenuous challenge in numerous catalytic applications. High resolution-HAADF-STEM coupled with in situ and tomographic analyses are undertaken on platinum (Pt) active phase supported on chlorinated alumina (γ-Al 2 O 3) with 0.3 and 1% w/w Pt loadings highlighting the formation of flat nanoparticles (NPs) of 0.9 nm diameter and Pt single atoms (SAs) in the reduced state. While SAs and weakly cohesive clusters are predominantly observed in the...

A. Chabani, C. Mehl, I. Cojan, R. Alais, D. Bruel (2020): Semi-automated component identification of a complex fracture network using a mixture of von Mises distributions: Application to the Ardeche margin (South-East France). Computers & Geosciences 137 104435.
Proposing a quantitative description of fracture main orientations is of prime interest for reservoir modeling. Manual sorting of fracture sets is time consuming and requires individual expertise. Semi automated methods for determination of the number of fracture sets are not developed in structural geology despite complex fracture networks being common. This study aims at demonstrating the input of mixture of von Mises (MvM) distributions to model complex fracture datasets, based on data from the Ardeche margin (7800 km2 SE France). An appraisal test selects the optimized number of...

A. Lelevic, V. Souchon, M. Moreaud, C. Lorentz, C. Geantet (2020): Gas chromatography vacuum ultraviolet spectroscopy: A review. Journal of Separation Science 43(1) 150—173.
Accelerated technological progress and increased complexity of interrogated matrices imposes a demand for fast, powerful, and resolutive analysis techniques. Gas chromatography has been for a long time a ‘go‐to’ technique for the analysis of mixtures of volatile and semi‐volatile compounds. Coupling of the several dimensions of gas chromatography separation has allowed to access a realm of improved separations in the terms of increased separation power and detection sensitivity. Especially comprehensive separations offer an insight into detailed sample composition for complex samples....

D. Jeulin (2020): Towards crack paths simulations in media with a random fracture energy. International Journal of Solids and Structures 184 279—286.

C. Gommes, Y. Jiao, A. Roberts, D. Jeulin (2020): Chord-length distributions cannot generally be obtained from small-angle scattering. Journal of Applied Crystallography 53(1) 127—132.

A. Fehri, S. Velasco-Forero, F. Meyer (2020): Combinatorial space of watershed hierarchies for image characterization. Pattern Recognition Letters 129 41—47.
We propose a framework for image characterization using hierarchies of segmentations. For this purpose, we structure the space of hierarchies using the Gromov–Hausdorff distance. We propose different ways of combining hierarchies and study their properties thanks to the GH distance. We then expose how to leverage the combinatorial space of hierarchies to derive efficient image representations. This framework opens a path for a controlled exploration and use of the combinatorial space of hierarchies.

R. Alais, P. Dokládal, A. Erginay, B. Figliuzzi, E. Decencière (2020): Fast macula detection and application to retinal image quality assessment. Biomedical Signal Processing and Control 55 101567.
In this article, we present a segmentation algorithm for assessing retinal image quality with respect to the visibility of the macular region. An image is considered of acceptable quality if the macular region is clearly visible and entirely in the field of view. Additionally, for acceptable images, the method is able to locate the fovea with a maximal error of 0.34 mm. The algorithm is based on a lightweight fully-convolutional network, several thousand times smaller than state-of-the-art networks investigated so far in preliminary studies. We obtain near-human performance for assessing...

S. Forest, F. Willot (2020): Preface to a Special Issue of the International Journal of Solids and Structures on Physics and Mechanics of Random Structures: From Morphology to Material Properties In honor of Professor Dominique Jeulin (Mines ParisTech). International Journal of Solids and Structures 184 1—2.
This volume gathers contributions presented at the International Workshop on Physics and Mechanics of Random Structures: From Morphology to Material Properties held on the Ile d’Oléron (Atlantic coast, France), June 17–22, 2018 (Willot and Forest, 2018; Oleron, 2019). This exceptional workshop was organized on the occasion of the (official) retirement of Prof. Dominique Jeulin from his position at Mines ParisTech, where he has been working for more than 30 years to the development of mathematical morphology and physics and mechanics of random media.

R. Rodriguez Salas, P. Dokládal, E. Dokladalova (2020): A Minimal Model for Classification of Rotated Objects with Prediction of the Angle of Rotation.
In classification tasks, the robustness against various image transformations remains a crucial property of the Convolutional Neural Networks (CNNs). It can be acquired using the data augmentation. It comes, however, at the price of the risk of overfitting and a considerable increase in training time. Consequently, other ways to endow CNN with invariance to various transformations-and mainly to the rotations-is an intensive field of study. This paper presents a new reduced rotation invariant classification model composed of two parts: a feature representation mapping and a classifier. We...

G. Ferri, S. Humbert, M. Digne, J.M. Schweitzer, M. Moreaud (2020): Aggregation morphological model with variable fractal dimension from colloidal system to porous material.
For the development of a new porous material such as catalytic carrier, the control of the textural properties is of fundamental importance. In order to move towards rational synthesis, it is necessary to better understand the physical phenomena that generate a defined solid structure. A contribute to this purpose can be achieved by studying the aggregation process inside colloidal suspensions, leading to porosity generation: this phenomenon can be described with a Brownian dynamics model that, for any set of chemical parameters, enables to evaluate the mass distribution and the fractal...


List of all publications from the CMM, recorded on the HAL depository under the tag ENSMP_CMM.

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