[ Information ] [ Publications ] [Signal processing codes] [ Signal & Image Links ]
[ Main blog: A fortunate hive ] [ Blog: Information CLAde ] [ Personal links ]
[ SIVA Conferences ] [ Other conference links ] [ Journal rankings ]
[ Tutorial on 2D wavelets ] [ WITS: Where is the starlet? ]
If you cannot find anything more, look for something else (Bridget Fountain)
Si vous ne trouvez plus rien, cherchez autre chose (Brigitte Fontaine)
Web www.laurent-duval.eu lcd.siva.free.fr

ICIP 2014: IEEE International Conference on Image Processing
Special session on Image Processing for Materials Characterization

Video conference Materials Science: from Materials Discovered by Chance to Made-To-Measure Materials
Yves Bréchet, Inaugural lecture, 17/01/2013

ICIP 2014 special session aims:

Materials science is evolving from materials discovered in Nature by chance to designed materials that repair themselves, adapt to their environment, capture and store energy or information, or help elaborate new devices. Materials are now designed from scratch with initial blueprints, starting from atoms and molecules. This evolution, at the confluence of science, technology, and engineering, is driven by the synergy of materials science and physics, mechanics, chemistry, biology, and engineering, with image processing taking part in this challenge. Indeed, the possibility of designing, analyzing, and modelling materials from images (or generally two- or three-dimensional modalities) has yielded important contributions to the materials-science field. The appearance of materials changes significantly with imaging techniques, depending on the scale of analysis, imaging settings, physical properties, as well as preparation of materials, and understanding these aspects is critical for material analysis and modelling. This special session will target relevant problems in material characterization that can be addressed with classical or advanced methods of signal and image processing with a focus on techniques that employ methods such as restoration, segmentation, mathematical morphology, texture analysis, multiscale and directional-feature extraction, color and multispectral processing, and stochastic models.

Referrers: [Nuit Blanche|MECANAT Groupe Français de Mécanique des Matériaux|SF2M /GFC : Congrès matériaux dans le monde|Riken Image Processing research team (events)]

Image Processing for Materials Characterization: Issues, Challenges and Opportunities [slides]

ICIP 2014: Mister Jack inclusion in a SEM catalyst image
Mister Jack inclusion in a SEM catalyst image

Authors: Maxime Moreaud, Laurent Duval, Camille Couprie, Dominique Jeulin, Jesús Angulo, and Hugues Talbot
This introductory paper aims at summarizing some problems and state-of-the-art techniques encountered in image processing for material analysis and design. Developing generic methods for this purpose is a complex task given the variability of the different image acquisition modalities (optical, scanning or transmission electron microscopy; surface analysis instrumentation, electron tomography, micro-tomography...), and material composition (porous, fibrous, granular, hard materials, membranes, surfaces and interfaces...). This paper presents an overview of techniques that have been and are currently developed to address this diversity of problems, such as segmentation, texture analysis, multiscale and directional features extraction, stochastic models and rendering, among others. Finally, it provides references to enter the issues, challenges and opportunities in materials characterization.

Keywords: Image Processing, Image-based Analysis, Materials science, Stochastic modelling, Surface Science, Texture Analysis, Work-flow

Structure Tensor Based Synthesis of Directional Textures for Virtual Material Design [slides]

Authors: Adib Akl, Charles Yaacoub, Marc Donias, Jean-Pierre Da Costa, Christian Germain
Exemplar-based texture synthesis schemes are promising for virtual material design. They provide impressive results in many cases, but fail in difficult situations with large and multi-scale patterns, or with long range directional variations. Since a prior synthesis of a geometric layer may help in the synthesis of the texture layer, a two-stage structure/texture synthesis algorithm is proposed. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplar’s data, and at the second stage, the synthesized tensor field is used to constrain the synthesis of the texture. Results show that the proposed approach not only yields better synthesized textures, but also successfully synthesizes the output texture in many situations where traditional algorithms fail to reproduce the exemplar’s patterns, which paves the way towards the synthesis of accurately large and multi-scale patterns as it is the case for pyrolytic carbon samples showing laminar structures observed by Transmission Electronic Microscopy.

Keywords: Non Parametric Texture Synthesis, Structure Tensor, Exemplar Based Synthesis, Virtual Material, Pyrolytic Carbon Simulation
ICIP 2014: Analysis and Synthesis of a composite material 
observed by Transmission Electronic Microscopy
Analysis and Synthesis of a composite material observed by Transmission Electronic Microscopy (texture, coherence, orientation).

Volume-Based Shape Analysis for Internal Microstructure of Steels [slides]

ICIP 2014: Shape analyses of an inclusion and its cracks in a bearing steel
Shape analyses of an inclusion and its cracks in a bearing steel. Left: a volume image obtained with a serial sectioning device. Upper right: segmentation and labelling. Bottom middle: orientation analysis. Bottom right: curvature analysis.

Authors: Norio Yamashita, Shin Yoshizawa, Hideo Yokota
In this paper, we propose a novel framework of analyzing the internal micro-structure of steel materials. Our framework consists of destructive imaging and geometric computing techniques. Imaging is based on a high-precision sectioning tool, and optical imaging with sub-micrometer resolution. We adapt geometry processing methods for segmented multimaterial volumes to obtain the structural features. We use our framework to image inclusions and cracks of ball-bearing steels. Our analysis indicates an interesting relationship between the concavity of inclusion shapes and crack initiation.

Keywords: Materials Genome, Image-based Shape Analysis, Multi-labeled Volumes, Steel, Inclusions and Cracks

Physics of MRF Regularization for Segmentation of Materials Microstructure Images (pdf) [slides]

Authors: Jeff Simmons, Craig Przybyla, Stephen Bricker, Dae Woo Kim, Mary Comer
The Markov Random Field (MRF) has been used extensively in Image Processing as a means of smoothing interfaces between differing regions in an image. The MRF applies a total boundary length ‘energy’ penalty that is subsequently minimized by an inversion algorithm. Minimum energy implies a force associated with boundaries, the sum of whichmust equal zero at every point at equilibrium. This requirement leads to long range structures, resulting from the short-range interactions of the MRF used to bias segmentation results. This work uses a simple Bayesian MRF regularized segmentation method to show that classical results from Surface Science are reproduced when segmenting regions of low contrast. This has implications, both in theMaterials Science and Image Processing fields.

Keywords: segmentation, priors, context-sensitive segmentation, materials science
ICIP 2014: Influence of the physics intrinsic to the MRF on an inpainted interface
Influence of the physics intrinsic to the MRF on an inpainted interface.

Image Processing In Experiments On, And Simulations Of Plastic Deformation Of Polycrystals [<slides]

Atomic structure of a ceria nanoparticle (copyright Rhodia).

Authors: Jonathan Lind, Anthony D. Rollett, Reeju Pokharel, Christopher Hefferan, Shiu-Fai Li, Ulrich Lienert, Robert Suter
Comparisons between experiments and simulations of deformation of polycrystalline materials reveal some interesting challenges [1]. Addressing first the image processing issues, electron back-scatter diffraction (EBSD) [2] relies heavily on image transformations of electron diffraction patterns. High energy diffraction microscopy (HEDM) [3] also relies on thresholding of the diffractograms for peak identification [4]. By contrast to the standard finite element method, an image-based approach [5] that relies on the Fast Fourier Transform (FFT) has started to be used for simulating plastic deformation because it offers a more efficient solution of the same equations (e.g. mechanical equilibrium). It is possible, for example, to import directly a measured 3D image from HEDM into the FFT simulation code and simulate with no need for the time-consuming step of creating a 3D mesh. Common filters applied to orientation maps in particular, include grain average strain, Kernel Average Misorientation (KAM), Grain Orientation Spread (GOS), Intragranular Grain Misorientation (IGM).

Keywords: X-ray Diffraction (XRD), High Energy Diffraction Microscopy (HEDM), Fast Fourier Transform (FFT), Orientation Gradients, Texture, Simulation, Edge Detection

Morse theory and persistent homology for topological analysis of 3D images of complex materialsMorse theory and persistent homology for topological analysis of 3D images of complex materials [slides]

Authors: Olaf Delgado-Friedrichs, Vanessa Robins, Adrian Sheppard
We summarize our latest advances in discrete Morse theory based algorithms for 3D image analysis. Starting with a discrete gradient vector field derived from the voxel values, we define topologically accurate and compatible versions of traditional skeletons and watershed partitions, that are simplified using persistent homology. Efficient implementations allow us to process real x-ray micro-CT data of rock cores and other materials.

Keywords: Discrete Morse theory, Skeletonisation, Watershed transform, Computational topology
ICIP 2014: Morse analysis of a 2D signed Euclidean distance image taken from x-ray micro-tomography
Morse analysis of a 2D signed Euclidean distance image (x-ray micro-tomography of Mt Gambier limestone).