Bildsegmentierung

Segmentierung in der Materialwissenschaft

Methoden in der Werkstoffkunde

Bildsegmentierung

Bildsegmentierung ist ein bildverarbeitungstechnischer Ansatz, um digitale Aufnahmen in bedeutungsvolle homogene Bereiche zu unterteilen. In der Werkstoffwissenschaft findet diese Methode Anwendung bei der Analyse von Mikrostrukturen, um Phasen, Defekte oder Materialinhomogenitäten automatisiert zu identifizieren.

Verwandte Tags

Neues

Deep learning image segmentation enabling automated quantitative microstructure analysis for manufacturing environments

Pham, G.-K. (Speaker)¹; Lampenscherf, S.²

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Multimodal Bioimaging for the Elemental and Molecular Investigation of gadolinium-based Contrast Agents in Nerve Tissue

Gödde, H. (Speaker)¹; Jeibmann, A.²; Rave, H.³; Linsen, L.³; Karst, U.¹

EWCPS 2025 - 20th European Winter Conference on Plasma Spectrochemistry

Large Scale Analysis of High-Resolution Transmission Electron Microscopy Data of Metallic Nanoparticles Using Artificial Intelligence

Gumbiowski, N. (Speaker)¹; Epple, M.¹; Heggen, M.²; Loza, K.¹

Combining Phase Field Modeling and Deep Learning for Accurate Modeling of Crystal Orientation in Solidification Microstructure obtained by Wire Arc Additive Manufacturing

Klöcker, H. (Speaker)¹; Aboleinein, H.¹; Bergheau, J.-M.²; Herbeaux, A.¹; Maurice, C.¹; Villani, A.¹

A generic grain boundary detection model for quantitative grain size analysis from materials microscopy images

Rathod, K. (Speaker)¹; Choudhary, A.K.¹; Jansche, A.¹; Trier, F.¹; Ketzer-Raichle, G.¹; Bernthaler, T.¹; Schneider, G.¹

Anwendungsbeispiele eines automatisierten 3D-Metallographie-Systems anhand der Rekonstruktion von Poren eines Sinterstahls und eines 316L-AM

Lemiasheuski, A. (V)¹

58. Metallographie-Tagung 2024 - Materialographie

Deep Learning for blur localization in light-optical microscopy images

Krawczyk, P. (Speaker)¹; Bernthaler, T.¹; Jansche, A.¹; Maniyanthotil, N.¹; Schneider, G.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Development of an Automated 3D Metallography System and its Application in Microstructure Analysis

Lemiasheuski, A. (Speaker)¹; Bajer, E.¹; Göbel, A.¹; Hesse, R.¹; Oder, G.¹; Bettge, D.¹

FEMS EUROMAT 2023

Reproducible and automated structure-property relationships of bainitic steels

Ackermann, M. (Speaker)¹; Iren, D.²; Krupp, U.¹; Shetty, D.¹; Wesselmecking, S.¹

Highlight Lecture

Quantifying the impact of calendering on the morphology of nano-structured $\mathrm{Na}_3\mathrm{V}_2 (\mathrm{PO}_4)_3/\mathrm{C}$-cathodes for sodium-ion batteries based on synchrotron tomography

Neumann, M. (Speaker)¹; Bauer, W.²; Binder, J.R.²; Hilger, A.³; Häringer, M.²; Manke, I.³; Osenberg, M.³; Schmidt, V.¹; Schneider, L.²

Beliebt

Quantitative evaluation of void content in polymer matrix composites by means of X-ray computed tomography

Plank, B. (V)¹; Berer, M.²; Heupl, S.³; Höglinger, M.³; Kastner, J.³; Längauer, M.³; Maurer, J.³; Savanadaiah, C.⁴; Stadler, H.⁵; Stelzer, P.⁶

Verbundwerkstoffe - 23. Symposium Verbundwerkstoffe und Werkstoffverbunde

Digital Image Processing for Textile Characterization of Flax Yarn Utilized in Natural Fiber Reinforced Plastics

Chavoshi, A. (V)¹; Fleischmann, M. (V)¹; Cebulla, H.¹; Kaufmann, J.¹

Verbundwerkstoffe - 23. Symposium Verbundwerkstoffe und Werkstoffverbunde

Deep learning image segmentation enabling automated quantitative microstructure analysis for manufacturing environments

Pham, G.-K. (Speaker)¹; Lampenscherf, S.²

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Preisverleihung

Best Paper Award der Praktischen Metallographie

Mogire, E. (V)¹; Mücklich, F. (V)²

55. Metallographie-Tagung 2021 - Materialographie

Highlight Lecture

Quantifying the impact of calendering on the morphology of nano-structured $\mathrm{Na}_3\mathrm{V}_2 (\mathrm{PO}_4)_3/\mathrm{C}$-cathodes for sodium-ion batteries based on synchrotron tomography

Neumann, M. (Speaker)¹; Bauer, W.²; Binder, J.R.²; Hilger, A.³; Häringer, M.²; Manke, I.³; Osenberg, M.³; Schmidt, V.¹; Schneider, L.²

Large Deformations of Metal Foams: Dynamic CT Results, Simulations and Modeling

Hümbert, M. (Speaker)¹; De Boever, W.²; Glatt, E.¹; Grießer, A.¹; Grimm-Strele, H.³; Hunter, L.⁴; Kabel, M.³; Rief, S.¹

A generic grain boundary detection model for quantitative grain size analysis from materials microscopy images

Rathod, K. (Speaker)¹; Choudhary, A.K.¹; Jansche, A.¹; Trier, F.¹; Ketzer-Raichle, G.¹; Bernthaler, T.¹; Schneider, G.¹

Autonomous metallographic analysis of steels and nickel alloys using artificial intelligence

Korpala, G. (V)¹; Prahl, U.¹

MaterialsWeek 2021

KI-gestützte Gefügeuntersuchung - wirklich besser?

Janovsky, A. (V)¹; Hesseling, C.²

56. Metallographie-Tagung 2022 - Materialographie

Combining Phase Field Modeling and Deep Learning for Accurate Modeling of Crystal Orientation in Solidification Microstructure obtained by Wire Arc Additive Manufacturing

Klöcker, H. (Speaker)¹; Aboleinein, H.¹; Bergheau, J.-M.²; Herbeaux, A.¹; Maurice, C.¹; Villani, A.¹

Consistent automatic evaluation of γ/γ' Ni-base superalloy microstructure parameters from micrographs and simulation data

Müller, M. (Speaker)¹; Fleck, M.¹; Glatzel, U.¹; Schleifer, F.¹

EuroSuperalloys 2022 - 4th European Symposium on Superalloys and their Applications

Machine learning methods for a better understanding of functional materials

Bernthaler, T.¹; Choudhary, A.-K.¹; Goll, D.¹; Grubesa, T.¹; Jansche, A.¹; Kini, A.¹; Schneider, G.¹

MaterialsWeek 2021

Quantitative Fraktographie unter Verwendung klassischer Verfahren, Topographie-Daten und Deep-Learning

Bettge, D. (V)¹; Sonntag, U. (V)²; Botsch, B.²; Hemmleb, M.³; Le, Q.-H.¹; Schmies, L.¹; Yarysh, A.¹

55. Metallographie-Tagung 2021 - Materialographie

Multimodal Bioimaging for the Elemental and Molecular Investigation of gadolinium-based Contrast Agents in Nerve Tissue

Gödde, H. (Speaker)¹; Jeibmann, A.²; Rave, H.³; Linsen, L.³; Karst, U.¹

EWCPS 2025 - 20th European Winter Conference on Plasma Spectrochemistry

3D reconstruction of hierarchical nanoporous gold from FIB tomography data using deep neural networks

Sardhara, T. (V)¹; Shkurmanov, A. (V)¹; Aydin, R.C.²; Cyron, C.J.¹; Ritter, M.¹

Verbundwerkstoffe - 23. Symposium Verbundwerkstoffe und Werkstoffverbunde

Erhöhung der Bildschärfe in Lichtmikroskopieaufnahmen mittels Deep Learning

Krawczyk, P. (V)¹; Jansche, A.¹; Bernthaler, T.¹; Schneider, G.¹

55. Metallographie-Tagung 2021 - Materialographie

Development of an Automated 3D Metallography System and its Application in Microstructure Analysis

Lemiasheuski, A. (Speaker)¹; Bajer, E.¹; Göbel, A.¹; Hesse, R.¹; Oder, G.¹; Bettge, D.¹

FEMS EUROMAT 2023

Reproducible and automated structure-property relationships of bainitic steels

Ackermann, M. (Speaker)¹; Iren, D.²; Krupp, U.¹; Shetty, D.¹; Wesselmecking, S.¹

Deep Learning for blur localization in light-optical microscopy images

Krawczyk, P. (Speaker)¹; Bernthaler, T.¹; Jansche, A.¹; Maniyanthotil, N.¹; Schneider, G.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Large Scale Analysis of High-Resolution Transmission Electron Microscopy Data of Metallic Nanoparticles Using Artificial Intelligence

Gumbiowski, N. (Speaker)¹; Epple, M.¹; Heggen, M.²; Loza, K.¹

Anwendungsbeispiele eines automatisierten 3D-Metallographie-Systems anhand der Rekonstruktion von Poren eines Sinterstahls und eines 316L-AM

Lemiasheuski, A. (V)¹

58. Metallographie-Tagung 2024 - Materialographie

© 2025