@Inproceedings{DelgadoFriedrichs_O_2014_p-icip_morse_tphta3dicm, Title = {Morse theory and persistent homology for topological analysis of {3D} images of complex materials}, Author = {Delgado-Friedrichs, Olaf and Robins, Vanessa and Sheppard, Adrian}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, File = {DelgadoFriedrichs_O_2014_p-icip_morse_tphta3dicm.pdf:DelgadoFriedrichs_O_2014_p-icip_morse_tphta3dicm.pdf:PDF;DelgadoFriedrichs_O_2014_p-icip_morse_tphta3dicm.pdf:References\\DelgadoFriedrichs_O_2014_p-icip_morse_tphta3dicm.pdf:PDF}, Month = {4872--4876}, Year = {2014}, Address = {Paris, France}, Abstract = {We develop topologically accurate and compatible definitions for the skeleton and watershed segmentation of a 3D digital object that are computed by a single algorithm. These definitions are based on a discrete gradient vector field derived from a signed distance transform. This gradient vector field is amenable to topological analysis and simplification via For-man's discrete Morse theory and provides a filtration that can be used as input to persistent homology algorithms. Efficient implementations allow us to process large-scale x-ray micro-CT data of rock cores and other materials.}, Doi = {10.1109/icip.2014.7025987}, ISBN = {http://id.crossref.org/isbn/978-1-4799-5751-4}, Owner = {duvall}, Timestamp = {2015.02.21.15.06}, Url = {http://dx.doi.org/10.1109/ICIP.2014.7025987} } @Inproceedings{Simmons_J_2014_p-icip_physics_mrfrsmmi, Title = {Physics of {MRF} regularization for segmentation of materials microstructure images}, Author = {Simmons, Jeff and Przybyla, Craig and Bricker, Stephen and Kim, Dae Woo and Comer, Mary}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, File = {Simmons_J_2014_p-icip_physics_mrfrsmmi.pdf:Simmons_J_2014_p-icip_physics_mrfrsmmi.pdf:PDF;Simmons_J_2014_p-icip_physics_mrfrsmmi.pdf:References\\Simmons_J_2014_p-icip_physics_mrfrsmmi.pdf:PDF}, Month = {Oct. 27-30,}, Pages = {4882--4886}, Year = {2014}, Address = {Paris, France}, 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. The minimization of energy implies a force associated with boundaries, the sum of which must equal zero at every point at equilibrium. This requirement leads to long range interactions, resulting from the short-range interactions of the MRF, which biases 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 the Materials Science and Image Processing fields.}, Doi = {10.1109/icip.2014.7025989}, ISBN = {http://id.crossref.org/isbn/978-1-4799-5751-4}, Owner = {duvall}, Timestamp = {2015.02.21.15.03}, Url = {http://dx.doi.org/10.1109/ICIP.2014.7025989} } @Inproceedings{Lind_J_2014_p-icip_image_pespdp, Title = {Image processing in experiments on, and simulations of plastic deformation of polycrystals}, Author = {Lind, Jonathan and Rollett, Anthony D. and Pokharel, Reeju and Hefferan, Christopher and Li, Shiu-Fai and Lienert, Ulrich and Suter, Robert}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, File = {Lind_J_2014_p-icip_image_pespdp.pdf:Lind_J_2014_p-icip_image_pespdp.pdf:PDF;Lind_J_2014_p-icip_image_pespdp.pdf:References\\Lind_J_2014_p-icip_image_pespdp.pdf:PDF}, Month = {Oct. 27-30,}, Pages = {4877--4881}, Year = {2014}, Address = {Paris, France}, 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).}, Doi = {10.1109/icip.2014.7025988}, ISBN = {http://id.crossref.org/isbn/978-1-4799-5751-4}, Owner = {duvall}, Timestamp = {2015.02.21.14.56}, Url = {http://dx.doi.org/10.1109/ICIP.2014.7025988} } @Inproceedings{Yamashita_N_2014_p-icip_volume-based_saims, Title = {Volume-based shape analysis for internal microstructure of steels}, Author = {Yamashita, Norio and Yoshizawa, Shin and Yokota, Hideo}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, File = {Yamashita_N_2014_p-icip_volume-based_saims.pdf:Yamashita_N_2014_p-icip_volume-based_saims.pdf:PDF;Yamashita_N_2014_p-icip_volume-based_saims.pdf:References\\Yamashita_N_2014_p-icip_volume-based_saims.pdf:PDF}, Month = {Oct.}, Pages = {4887--4891}, Year = {2014}, Address = {Paris, France}, 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.}, Doi = {10.1109/icip.2014.7025990}, ISBN = {http://id.crossref.org/isbn/978-1-4799-5751-4}, Owner = {duvall}, Timestamp = {2015.02.21.14.54}, Url = {http://dx.doi.org/10.1109/ICIP.2014.7025990} } @Inproceedings{Akl_A_2014_p-icip_structure_tbsdtvmd, Title = {Structure tensor based synthesis of directional textures for virtual material design}, Author = {Akl, Adib and Yaacoub, Charles and Donias, Marc and Da Costa, Jean-Pierre and Germain, Christian}, File = {Akl_A_2014_p-icip_structure_tbsdtvmd.pdf:Akl_A_2014_p-icip_structure_tbsdtvmd.pdf:PDF;Akl_A_2014_p-icip_structure_tbsdtvmd.pdf:References\\Akl_A_2014_p-icip_structure_tbsdtvmd.pdf:PDF}, Month = {Oct. 27-30,}, Pages = {4867--4871}, Year = {2014}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, Address = {Paris, France}, 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.}, Doi = {10.1109/icip.2014.7025986}, ISBN = {http://id.crossref.org/isbn/978-1-4799-5751-4}, Owner = {duvall}, Timestamp = {2015.02.21.14.49}, Url = {http://dx.doi.org/10.1109/ICIP.2014.7025986} } @Inproceedings{Duval_L_2014_p-icip_image_pmcico, Title = {Image Processing for Materials Characterization: Issues, Challenges and Opportunities}, Author = {L. Duval and M. Moreaud and C. Couprie and D. Jeulin and H. Talbot and J. Angulo}, Booktitle = {IEEE International Conference on Image Processing (ICIP 2014)}, File = {Duval_L_2014_p-icip_image_pmcico.pdf:Duval_L_2014_p-icip_image_pmcico.pdf:PDF;Duval_L_2014_p-icip_image_pmcico.pdf:References\\Duval_L_2014_p-icip_image_pmcico.pdf:PDF}, Month = {Oct. 27-30,}, Pages = {4862--4866}, Year = {2014}, Address = {Paris, France}, Keywords = {Image segmentation;Materials;Microstructure;Scanning electron microscopy;Three-dimensional displays;Image Processing;Image-based Analysis;Materials science;Stochastic modeling;Surface Science;Texture Analysis;Work-flow}, 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.}, Doi = {10.1109/ICIP.2014.7025985}, Owner = {duvall}, Timestamp = {2015.02.03.16.43} }