Publications du Centre de Morphologie Mathématique

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V. Machairas (2016): Waterpixels and their application to image segmentation learning.
In this work, we would like to provide a general method for automatic semantic segmentation, which could adapt itself to any image database in order to be directly used by non-experts in im- age analysis (such as biologists). To address this problem, we first propose to use pixel classification, a classic approach based on supervised learning, where the aim is to assign to each pixel the label of the object it belongs to. Features describing each pixel properties, and which are used to determine the class label, are often computed on a fixed-shape support (such as a centered window), which...

F. Barbaresco, T. Forget, E. Chevallier, J. Angulo (2016): Doppler spectrum segmentation of radar sea clutter by mean-shift and information geometry metric. 17th International Radar Symposium (IRS'16), Krakow (Poland) 1 — 6.
Radar sea clutter inhomogeneity in range is characterized by Doppler mean and spectrum width variations. We propose a new approach for robust statistical density estimation and segmentation of sea clutter Doppler spectrum. In each range cell, Doppler is characterized by a Toeplitz Hermitian Positive Definite covariance matrix that is coded in Poincaré's unit poly-disk and we use adaptation of standard kernel methods to density estimation on this specific Riemannian manifold. Based on this non-parametric approach to estimate statistical density of Doppler Spectrum, we address the problem of...

E. Chevallier, T. Forget, F. Barbaresco, J. Angulo (2016): Kernel Density Estimation on the Siegel Space with an Application to Radar Processing. Entropy 18(11) 396.
This paper studies probability density estimation on the Siegel space. The Siegel space is a generalization of the hyperbolic space. Its Riemannian metric provides an interesting structure to the Toeplitz block Toeplitz matrices that appear in the covariance estimation of radar signals. The main techniques of probability density estimation on Riemannian manifolds are reviewed. For computational reasons, we chose to focus on the kernel density estimation. The main result of the paper is the expression of Pelletier’s kernel density estimator. The computation of the kernels is made possible by...

D. Tuccillo, M. Huertas-Company, E. Decencìère, S. Velasco-Forero (2016): Deep learning for studies of galaxy morphology. IAU Symposium 325 on Astroinformatics, Sorrente (Italy) 12(S325) 191 — 196.
Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide paramet-ric properties of Hubble Space Telescope...

F. Meyer (2016): Adjonctions : érosion et dilatation géodésique.

F. Meyer (2016): Les adjonctions: une approche algébrique.

M. Bilodeau, C. Clienti, F. Coelho, S. Guelton, F. Irigoin, R. Keryell, F. Lemonnier (2016): FREIA ANT-AF-2007-004. Congrès ANR, Lyon (France).

J.C. Bricola (2016): Depth map estimation from stereo images & mathematical morphology.
In this thesis, we introduce new approaches dedicated to the computation of depth maps associated with a pair of stereo images. The main difficulty of this problem resides in the establishment of correspondences between the two stereoscopic images. Indeed, it is difficult to ascertain the relevance of matches occurring in homogeneous areas, whilst matches are infeasible for pixels occluded in one of the stereo views. In order to handle these two problems, our methods are composed of two steps. First, we search for reliable depth measures, by comparing the two images of the stereo pair with...

F. Willot, B. Abdallah, D. Jeulin (2016): The Permeability of Boolean Sets of Cylinders. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles 71(4).
Numerical and analytical results on the permeability of Boolean models of randomly-oriented cylinders with circular cross-section are reported. The present work investigates cylinders of prolate (highly-elongated) and oblate (nearly flat) types. The fluid flows either inside or outside of the cylinders. The Stokes flow is solved using full-fields Fourier-based computations on 3D binarized microstructures. The permeability is given for varying volume fractions of pores. A new upper-bound is derived for the permeability of the Boolean model of oblate cylinders. The behavior of the permeability...

G. Noyel, D. Jeulin, E. Parra-Denis, M. Bilodeau (2016): 在轮胎的内部表面上的浮凸元素特征的快速分析方法.
Method of fast analysis of the relief elements featuring on the interior surface of a tyre, said method comprising the steps in the course of which: A- the three dimensional image of said surface is captured by assigning each pixel of the image a grey level value proportional to the topographical elevation of this point, so as to obtain a starting image, B- the image of the surface captured is transformed into an orthogonal reference frame (OXY) in which the abscissa axis (OX) represents the circumferential values, and the ordinate axis (OY) represents the radial values, C- each pixel of the...

G. Franchi (2016): Spatial machine learning applied to multivariate and multimodal images.
This thesis focuses on multivariate spatial statistics and machine learning applied to hyperspectral and multimodal and images in remote sensing and scanning electron microscopy (SEM). In this thesis the following topics are considered: Fusion of images: SEM allows us to acquire images from a given sample using different modalities. The purpose of these studies is to analyze the interest of fusion of information to improve the multimodal SEM images acquisition. We have modeled and implemented various techniques of image fusion of information, based in particular on spatial regression theory....

G. Franchi, J. Angulo, D. Sejdinovic (2016): Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix (United States).
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions - by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate each pixel to an empirical distribution of its neighbouring pixels, a judicious representation of which in an RKHS, in conjunction with the spectral information contained in the pixel itself,...

G. Franchi, J. Angulo (2016): A deep spatial/spectral descriptor of hyperspectral texture using scattering transform. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix (United States).
A technique to describe the spatial / spectral features of hyperspectral images is introduced. These descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations, so called spatial invariances. Moreover, we also consider spectral invariances which are related to the composition of the pixels. Our approach is based on the scattering transform, which provides an useful framework for deep learning classification. The goal through these descriptors is to improve pixel-wise classification of hyperspectral images.

J. Corvo, J. Angulo, J. Breugnot, S. Bordes, B. Closs (2016): Common reduced spaces of representation applied to multispectral texture analysis in cosmetology. Multimodal Biomedical Imaging XI, San Francisco (United States).

G. Franchi, J. Angulo (2016): Morphological Principal Component Analysis for Hyperspectral Image Analysis. ISPRS International Journal of Geo-Information 5(6).

E. Chevallier, J. Angulo (2016): The Irregularity Issue of Total Orders on Metric Spaces and Its Consequences for Mathematical Morphology. Journal of Mathematical Imaging and Vision 54(3) 344 — 357.

E. Chevallier, A. Chevallier, J. Angulo (2016): N-ary Mathematical Morphology. Mathematical Morphology - Theory and Applications 1(1).

B. Figliuzzi, W.H.R. Chan, C.R. Buie, J.L. Moran (2016): Nonlinear electrophoresis in the presence of dielectric decrement. Physical Review E 94(2).
The nonlinear phenomena that occur in the electric double layer (EDL) that forms at charged surfaces strongly influence electrokinetic effects, including electro-osmosis and electrophoresis. In particular, saturation effects due to either dielectric decrement or ion crowding effects are of paramount importance. Dielectric decrement significantly influences the ionic concentration in the EDL at high ζ potential, leading to the formation of a condensed layer near the particle's surface. In this article, we present a model incorporating both steric effects due to the finite size of ions and...

D. Jeulin (2016): Morphological probabilistic hierarchies for texture segmentation. Mathematical Morphology - Theory and Applications 1(Issue 1) 216—234.
A general methodology is introduced for texture segmentation in binary, scalar, or multispectral images. Textural information is obtained from morphological operations on images. Starting from a fine partition of the image in regions, hierarchical segmentations are designed in a probabilistic framework by means of probabilistic distances conveying the textural information, and of random markers accounting for the morphological content of the regions and of their spatial arrangement. The probabilistic hierarchies are built from binary or multiple fusion of regions.

D. Jeulin (2016): Iterated Boolean random varieties and application to fracture statistics models. Applications of Mathematics Volume 61, Issue 4 pp 363–386.
Models of random sets and of point processes are introduced to simulate some specific clustering of points, namely on random lines in R2 and R3 and on random planes in R3. The corresponding point processes are special cases of Cox processes. The generating distribution function of the probability distribution of the number of points in a convex set K and the Choquet capacity T (K) are given. A possible application is to model point defects in materials with some degree of alignment. Theoretical results on the probability of fracture of convex specimens in the framework of the weakest link...

S. Drouyer, S. Beucher, M. Bilodeau, M. Moreaud, L. Sorbier (2016): A morphological approach for texture detection, application to SEM stereo reconstruction. The 16th European Microscopy Congress 2016, Lyon (France).

B. Figliuzzi, M. Bilodeau, J. Angulo, S. Velasco-Forero (2016): A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra. Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Leecy (Italy) Lecture Notes in COmputer Science(0016 2016) 263—274.
In this article, we present a Bayesian algorithm for endmember extraction and abundance estimation in situations where prior information is available for the abundances. The algorithm is considered within the framework of the linear mixing model. The novelty of this work lies in the introduction of bound parameters which allow us to introduce prior information on the abundances. The estimation of these bound parameters is performed using a simulated annealing algorithm. The algorithm is illustrated by simulations conducted on synthetic AVIRIS spectra and on the SAMSON dataset.

A. Belhedi, B. Marcotegui (2016): Adaptive scene-text binarization on images captured by smartphones. Image Processing, IET 10(7).
We address, in this paper, a new adaptive binarization method on images captured by smartphones. This work is part of an application for visually impaired people assistance, that aims at making text information accessible to people who cannot read it. The main advantage of the proposed method is that the windows underlying the local thresh-olding process are automatically adapted to the image content. This avoids the problematic parameter setting of local thresholding approaches, difficult to adapt to a heterogeneous database. The adaptive windows are extracted based on ultimate opening (a...

A. Serna, B. Marcotegui, J. Hernández (2016): Segmentation of Façades from Urban 3D Point Clouds using Geometrical and Morphological Attribute-based Operators. ISPRS International Journal of Geo-Information 5(1).
3D building segmentation is an important research issue in the remote sensing community with relevant applications to urban modeling, cloud-to-cloud and cloud-to-model registration, 3D cartography, virtual reality, cultural heritage documentation, among others. In this paper, we propose automatic, parametric and robust approaches to segment façades from 3D point clouds. Processing is carried out using elevation images and 3D decomposition, and the final result can be reprojected onto the 3D point cloud for visualization or evaluation purposes. Our methods are based on geometrical and...

J.X. Zhao, C. Thierry, E. Decencière, D. Jeulin, D. Cárdenas-Peña, L. Silva (2016): Direct multiphase mesh generation from 3D images using anisotropic mesh adaptation and a redistancing equation. Computer Methods in Applied Mechanics and Engineering 309 288–306.
In this paper, a new methodology to build automatically 3D adapted meshes, ready for numerical simulations and directly from images, is proposed. It is based on the Immersed Image Method, which interpolates the image information on an initial mesh and combines it with parallel automatic anisotropic mesh adaptation with a control of the number of mesh nodes. Simultaneously, a smooth redistancing technique, based on the resolution of a Hamilton–Jacobi equation, is developed to produce phase functions for the objects to be detected in the image. The proposed methodology is applied on mesh...

H. WANG (2016): Morphological Modeling and Transport Properties of mesoporous alumina.
In a work made at Centre de Morphologie Mathématique and IFPEN, we study the microstructure and physical properties of mesoporous alumina. This is a catalyst carrier used in the petroleum refining industry. Highly porous, it contains disordered ''platelets'' at the nanoscale. The mass transport properties of the catalyst carrier are strongly influenced by the morphology of the porous microstructure. We focus on the modeling of the microstructure and of transport properties of mesoporous alumina, using numerical and theoretical tools derived from image analysis and random sets models. On the...

A. Fehri, S. Velasco-Forero, F. Meyer (2016): Automatic Selection of Stochastic Watershed Hierarchies. European Conference of Signal Processing (EUSIPCO), Budapest (Hungary).
The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set...

J.C. Bricola, M. Bilodeau, S. Beucher (2016): Morphological processing of stereoscopic image superimpositions for disparity map estimation.
This paper deals with the problem of depth map computation from a pair of rectified stereo images and presents a novel solution based on the morphological processing of disparity space volumes. The reader is guided through the four steps composing the proposed method: the segmentation of stereo images, the diffusion of superimposition costs controlled by the segmentation, the resulting generation of a sparse disparity map which finally drives the estimation of the dense disparity map. An objective evaluation of the algorithm's features and qualities is provided and is accompanied by the...

V. Bortolussi, F. Borit, A. Chesnaud, M. Jeandin, M. Faessel, B. Figliuzzi, F. Willot, K. Roche, G. Surdon (2016): Cold spray of metal-polymer composite coatings onto carbon fiber-reinforced polymer (CFRP). International Thermal Spray Conference 2016 (ITSC 2016), Shanghai (China) 7 p..
The growing use of Polymer-Matrix Composite (PMC) materials within transport industry raises new security concerns, especially those due to lightning. To protect these electrically insulating materials, conductive coatings can be applied. Due to the high level of required properties, cold spray is believed to be an effective way to achieve these coatings. Recent studies showed that obstacles remained to be overcome when cold spraying metallic particles onto Carbon Fiber-Reinforced Polymer (CFRP). These are rather due to a poor adhesion of metallic particles onto carbon fibers, which prevents...

A. Giachetti, F. Fornasa, F. Parezzan, A. Saletti, L. Zambaldo, L. Zanini, F. Achilles, A.E. Ichim, F. Tombari, N. Navab, S. Velasco-Forero (2016): Shrec'16 Track: Retrieval of Human Subjects from Depth Sensor Data. Eurographics Workshop on 3D Object Retrieval, Lisbon (Portugal) 1—6.
In this paper we report the results of the SHREC 2016 contest on "Retrieval of human subjects from depth sensor data". The proposed task was created in order to verify the possibility of retrieving models of query human subjects from single shots of depth sensors, using shape information only. Depth acquisition of different subjects were realized under different illumination conditions, using different clothes and in three different poses. The resulting point clouds of the partial body shape acquisitions were segmented and coupled with the skeleton provided by the OpenNI software and provided...

B.R. Kiran, B. Stanciulescu, J. Angulo (2016): Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorization. BIOIMAGING 2016, Rome (Italy) 2(77-84) 8.
Hyperspectral images of high spatial and spectral resolutions are employed to perform the challenging task of brain tissue characterization and subsequent segmentation for visualization of in-vivo images. Each pixel is a high-dimensional spectrum. Working on the hypothesis of pure-pixels on account of high spectral resolution, we perform unsupervised clustering by hierarchical non-negative matrix factorization to identify the pure-pixel spectral signatures of blood, brain tissues, tumor and other materials. This subspace clustering was further used to train a random forest for subsequent...

V. Machairas, T. Baldeweck, T. Walter, E. Decencière (2016): NEW GENERAL FEATURES BASED ON SUPERPIXELS FOR IMAGE SEGMENTATION LEARNING. International Symposium on Biomedical Imaging, Prague (Czech Republic).
Segmenting an image is usually one of the major and most challenging steps in the pipeline of biomedical image analysis. One classical and promising approach is to consider seg-mentation as a classification task, where the aim is to assign to each pixel the label of the objects it belongs to. Pixels are therefore described by a vector of features, where each feature is calculated on the pixel itself or, more frequently, on a sliding window centered on the pixel. In this work, we propose to replace the sliding window by superpixels, i.e. regions which adapt to the image content. We call the...

T. Chabardes, P. Dokládal, M. Faessel, M. Bilodeau (2016): A parallel, O(n), algorithm for unbiased, thin watershed. IEEE International Conference on Image Processing, Phoenix (United States).
The watershed transform is a powerful tool for morphological segmentation. Most common implementations of this method involve a strict hierarchy on gray tones in processing the pixels composing an image. Those dependencies complexify the efficient use of modern computational architectures. This paper aims at answering this problem by introducing a new way of simulating the waterflood that preserves the locality of data to be processed. We propose a region growth algorithm based on arrowing graphs that is strictly linear despite the valuation domain of input images. Simultaneous and disorderly...

B. Figliuzzi, D. Jeulin, M. Faessel, F. Willot, M. Koishi, N. Kowatari (2016): Modelling the microstructure and the viscoelastic behaviour of carbon black filled rubber materials from 3D simulations. Technische Mechanik 32(1-2) 22—46.
Volume fraction and spatial repartition of fillers impact the physical properties of rubber. Extended percolating networks of nano-sized fillers significantly modify the macroscopic mechanical properties of rubbers. Random models that describe the multiscale microstructure of rubber and efficient Fourier-based numerical algorithms are combined to predict the material’s mechanical properties. From TEM image analysis, various types of multiscale models were proposed and validated, accounting for the non-homogeneous distribution of fillers: in the present work, aggregates are located outside...

B. Abdallah, F. Willot, D. Jeulin (2016): Morphological modeling of three-phase microstructures of anode layers using SEM images. Journal of Microscopy 263(1) 51—63.
A general method is proposed to model 3D microstructures representative of three-phase anode layers used in fuel cells. The models are based on SEM images of cells with varying morphologies. The materials are first characterized using three morphological measurements: (cross-)covariances, granulometry and linear erosion. They are measured on segmented SEM images, for each of the three phases. Second, a generic model for three-phase materials is proposed. The model is based on two independent underlying random sets which are otherwise arbitrary. The validity of this model is verified using the...

J. Angulo (2016): Generalised morphological image diffusion. Nonlinear Analysis: Theory, Methods and Applications 134.
Relationships between linear and morphological scale-spaces have been considered by various previous works. The aim of this paper is to study how to generalise the diffusion-based approaches in order to introduce nonlinear filters whose effects mimic the asymmetric behaviour of morphological dilation and erosion, as well as other evolved morphological filters. A methodology based on the counter-harmonic mean is adopted here. Details of numerical implementation are discussed and results are provided to illustrate the various studied cases: isotropic, nonlinear and coherence-enhancing...

G. Franchi, J. Angulo (2016): Morphological Principal Component Analysis for Hyperspectral Image Analysis. ISPRS International Journal of Geo-Information 5(6) 83.
This paper deals with a problem of dimensionality reduction for hyperspectral images using the principal component analysis. Hyper-spectral image reduction is improved by adding structural/spatial information to the spectral information, by means of mathematical morphology tools. Then it can be useful in supervised classification for instance. The key element of the approach is the computation of a covariance matrix which integrates simultaneously both spatial and spectral information. Thanks to these new covariance matrices, new features can be extracted. To prove the efficiency of these new...

N. Lermé, P. Dokládal (2016): Geometry Analysis of Superconducting Cables For The Optimization of Global Performances. ICPRAM, Rome (Italy).
Superconducting cables have now become a mature technology for energy transport, high-field magnets (MRI, LHC) and fusion applications (ToreSupra, and eventually ITER and DEMO). The superconductors are extremely brittle and suffer from electrical damages brought by mechanical strain induced by electromagnetic field that they generate. An optimal wiring architecture, obtained by simulation, can limit these damages. However, the simulation is a complex process and needs validation. This validation is performed on real 3D samples by the means of image processing. Within this objective, this...

J. Escoda, F. Willot, D. Jeulin, J. Sanahuja, C. Toulemonde (2016): Influence of the multiscale distribution of particles on elastic properties of concrete. International Journal of Engineering Science 98 60—71.
The mechanical role of the shape of the aggregates, and their spatial distribution in concrete materials is examined. The effect on the macroscopic mechanical response as well as on the local stress fields are investigated by numerical means, making use of a recently developed Poisson-polyhedra model in which the aggregates have polyhedral shapes. Comparison is made with previous microstructure models of spheres. Full-field computations are carried out on large volume size using Fourier methods. The field maps are used to determine zones of highest stress. Furthermore, the scale separation...

N. Lermé, F. Rossant, I. Bloch, M. Paques, E. Koch, J. Benesty (2016): A Fully Automatic Method For Segmenting Retinal Arteries in Adaptive Optics Images. Pattern Recognition Letters 72 72–81.
Adaptive optics imaging of the retina has recently proven its capability to image micrometric structures such as blood vessels, involved in common ocular diseases. In this paper, we propose an approach for automatically segmenting the walls of retinal arteries in the images acquired with this technology. The walls are modeled as four curves approximately parallel to a previously detected reference line located near the vessel center (axial reflection). These curves are first initialized using a tracking procedure and then more accurately positioned using an active contour model embedding a...


Liste complète des publications du CMM, enregistrées sur HAL ("tag": ENSMP_CMM).

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