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.
Authors: Maxime Moreaud, Laurent Duval, Camille Couprie, Dominique Jeulin, Jesús Angulo, and Hugues Talbot Abstract:
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.
Authors: Adib Akl, Charles Yaacoub, Marc Donias, Jean-Pierre Da Costa, Christian Germain Abstract:
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
Analysis and Synthesis of a composite material
observed by Transmission Electronic Microscopy (texture, coherence, orientation).
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 Abstract:
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.
Authors: Jeff Simmons, Craig Przybyla, Stephen Bricker, Dae Woo Kim, Mary Comer Abstract:
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.
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 Abstract:
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
Authors: Olaf Delgado-Friedrichs, Vanessa Robins, Adrian Sheppard Abstract:
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.
