Mike Marsh
Mike Marsh
Object Research Systems
marshimaging.com의 이메일 확인됨 - 홈페이지
Digital rock physics benchmarks—Part I: Imaging and segmentation
H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ...
Computers & Geosciences 50, 25-32, 2013
Digital rock physics benchmarks—Part II: Computing effective properties
H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ...
Computers & Geosciences 50, 33-43, 2013
Structural clusters of evolutionary trace residues are statistically significant and common in proteins
S Madabushi, H Yao, M Marsh, DM Kristensen, A Philippi, ME Sowa, ...
Journal of molecular biology 316 (1), 139-154, 2002
X-ray CT and laboratory measurements on glacial till subsoil cores: assessment of inherent and compaction-affected soil structure characteristics
M Lamandé, D Wildenschild, FE Berisso, A Garbout, M Marsh, P Moldrup, ...
Soil Science 178 (7), 359-368, 2013
Steps toward automated deprocessing of integrated circuits
EL Principe, N Asadizanjani, D Forte, M Tehranipoor, R Chivas, ...
ISTFA 2017: Proceedings from the 43rd International Symposium for Testing …, 2017
Correlative X-ray and electron microscopy for multi-scale characterization of heterogeneous shale reservoir pore systems
J Goral, I Miskovic, J Gelb, M Marsh
AAPG Special Volumes, 2016
Poromechanics investigation at pore-scale using digital rock physics laboratory
S Zhang, N Saxena, P Barthelemy, M Marsh, G Mavko, T Mukerji
Proc., The Proceedings of 2011 COMSOL Conference in Stuttgart, 2011
Application of deep learning convolutional neural networks for internal tablet defect detection: high accuracy, throughput, and adaptability
X Ma, N Kittikunakorn, B Sorman, H Xi, A Chen, M Marsh, A Mongeau, ...
Journal of Pharmaceutical Sciences 109 (4), 1547-1557, 2020
Dragonfly as a platform for easy image-based deep learning applications
R Makovetsky, N Piche, M Marsh
Microscopy and Microanalysis 24 (S1), 532-533, 2018
Dragonfly SegmentationTrainer-A General and User-Friendly Machine Learning Image Segmentation Solution
N Piche, I Bouchard, M Marsh
Microscopy and Microanalysis 23 (S1), 132-133, 2017
Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning
A Badran, D Marshall, Z Legault, R Makovetsky, B Provencher, N Piché, ...
Journal of Materials Science 55 (34), 16273-16289, 2020
Automated voice system and method
H Hutchinson, M Marsh, W McMaster
US Patent App. 12/190,643, 2010
Dragonfly as a Flexible Platform for Interpreting and Processing Hyperspectral and other High-dimensional Images
N Piche, F Cote, E Yen, M Marsh
Microscopy and Microanalysis 24 (S1), 560-561, 2018
Forget About Cleaning up Your Micrographs: Deep Learning Segmentation is Robust to Image Artifacts
P Dong, B Provencher, N Basim, N Piché, M Marsh
Microscopy and Microanalysis 26 (S2), 1468-1469, 2020
Workflow Automation and Portability Enable High Throughput Image Processing and Segmentation for Cell Biology Systems
B Provencher, R Makovetsky, E Yen, N Piché, M Marsh
Microscopy and Microanalysis 25 (S2), 1388-1389, 2019
Simplifying and Streamlining Large-Scale Materials Image Processing with Wizard-Driven and Scalable Deep Learning
B Provencher, N Piché, M Marsh
Microscopy and Microanalysis 25 (S2), 402-403, 2019
Extending Monte Carlo Simulations of Electron Microscopy Images and Hyperspectral Images in a User-Friendly Framework
S Rudinsky, Y Yuan, R Gauvin, N Piché, M Marsh
Microscopy and Microanalysis 25 (S2), 222-223, 2019
Macro to nano: Correlative X-ray and electron microscopy for multi-length scale characterization of geomaterials
J Gelb, R Cicha-Szot, I Matyasik, G Leśniak, S Kowalska, ...
SCA2014-061, 1-7, 2014
Image segmentation for advanced characterization of 3D FIB-SEM reconstructions
M Marsh
Microscopy and Microanalysis 18 (S2), 630-631, 2012
Survey of recent advances in digital rock physics–benefits of DRP and their application to reservoir characterization
R Cicha-Szot, A Steinbach, M Marsh, J Gelb, S Linden, A Wiegmann, ...
presentation at the Geopetrol Conference held in Zakopane-Kościelisko …, 0
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