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Rebouças EDS, Marques RCP, Braga AM, Oliveira SAF, de Albuquerque VHC, Rebouças Filho PP. New level set approach based on Parzen estimation for stroke segmentation in skull CT images. Soft comput 2018. [DOI: 10.1007/s00500-018-3491-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Bulgarevich DS, Tsukamoto S, Kasuya T, Demura M, Watanabe M. Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures. Sci Rep 2018; 8:2078. [PMID: 29391483 PMCID: PMC5794901 DOI: 10.1038/s41598-018-20438-6] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 01/18/2018] [Indexed: 12/05/2022] Open
Abstract
For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.
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Affiliation(s)
- Dmitry S Bulgarevich
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan.
- Research Center for Development of Far-Infrared Region, University of Fukui, Fukui, 3-9-1, Bunkyo, 910-8507, Japan.
| | - Susumu Tsukamoto
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan
| | - Tadashi Kasuya
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Masahiko Demura
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan
| | - Makoto Watanabe
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
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Detection of the Magnetic Easy Direction in Steels Using Induced Magnetic Fields. METALS 2016. [DOI: 10.3390/met6120317] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels. METALS 2016. [DOI: 10.3390/met6070164] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2388-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Automatic microstructural characterization and classification using dual tree complex wavelet-based features and Bees Algorithm. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2188-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Nunes TM, de Albuquerque VHC, Papa JP, Silva CC, Normando PG, Moura EP, Tavares JMR. Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals. EXPERT SYSTEMS WITH APPLICATIONS 2013; 40:3096-3105. [DOI: 10.1016/j.eswa.2012.12.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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