Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets
Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these...
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| Published in | Microscopy and microanalysis Vol. 25; no. 4; pp. 924 - 941 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, USA
Cambridge University Press
01.08.2019
Oxford University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1431-9276 1435-8115 1435-8115 |
| DOI | 10.1017/S1431927619014569 |
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| Abstract | Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system. |
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| AbstractList | Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system. Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system. Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system. |
| Author | Hobbs, Toren J. Brust, Alexander F. Niezgoda, Stephen R. Payton, Eric J. |
| Author_xml | – sequence: 1 givenname: Alexander F. surname: Brust fullname: Brust, Alexander F. organization: 1Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA – sequence: 2 givenname: Eric J. surname: Payton fullname: Payton, Eric J. organization: 2Air Force Research Laboratory, Materials and Manufacturing Directorate, Dayton, OH 45433, USA – sequence: 3 givenname: Toren J. surname: Hobbs fullname: Hobbs, Toren J. organization: 1Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA – sequence: 4 givenname: Stephen R. surname: Niezgoda fullname: Niezgoda, Stephen R. email: niezgoda.6@osu.edu organization: 1Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31210120$$D View this record in MEDLINE/PubMed |
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| Keywords | segmentation atomic clustering Bayesian statistics graph cutting |
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| SubjectTerms | Algorithms Automation Bayesian analysis Clustering Computer applications Data analysis Datasets Identification Image processing Image reconstruction Image segmentation Inverse problems Materials information Materials science Maximum flow Microscopy Nodes Software and Instrumentation |
| Title | Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets |
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