Efficiency of template matching methods for Multiple-Point Statistics simulations
Almost all Multiple-Point Statistic (MPS) methods use internally a template matching method to select patterns that best match conditioning data. The purpose of this paper is to analyze the performances of ten of the most frequently used template matching techniques in the framework of MPS algorithm...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197421000124 |
| _version_ | 1818736820706344960 |
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| author | Mansoureh Sharifzadeh Lari Julien Straubhaar Philippe Renard |
| author_facet | Mansoureh Sharifzadeh Lari Julien Straubhaar Philippe Renard |
| author_sort | Mansoureh Sharifzadeh Lari |
| collection | DOAJ |
| description | Almost all Multiple-Point Statistic (MPS) methods use internally a template matching method to select patterns that best match conditioning data. The purpose of this paper is to analyze the performances of ten of the most frequently used template matching techniques in the framework of MPS algorithms. Performance is measured in terms of computing efficiency, accuracy, and memory usage. The methods were tested with both categorical and continuous training images (TI). The analysis considers the ability of those methods to locate rapidly and with minimum error a data event with a specific proportion of known pixels and a certain amount of noise.Experiments indicate that the Coarse to Fine using Entropy (CFE) method is the fastest in all configurations. Skipping methods are efficient as well. In terms of accuracy, and without noise all methods except CFE and cross-correlation (CC) perform well. CC is the least accurate in all configurations if the TI is not normalized. This method performs better when normalized training images are used. The Binary Sum of Absolute Difference is the most robust against noise. Finally, in terms of memory usage, CFE is the worst among the ten methods that were tested; the other methods are not significantly different. |
| first_indexed | 2024-12-18T00:43:14Z |
| format | Article |
| id | doaj.art-a2f27432e3da4fcfb65bbeb4751cc5b5 |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-12-18T00:43:14Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-a2f27432e3da4fcfb65bbeb4751cc5b52022-12-21T21:26:50ZengElsevierApplied Computing and Geosciences2590-19742021-09-0111100064Efficiency of template matching methods for Multiple-Point Statistics simulationsMansoureh Sharifzadeh Lari0Julien Straubhaar1Philippe Renard2Department of Electrical and Computer Engineering, University of Hormozgan, Bandarabbas, 7916193145, IranCenter of Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile Argand 11, 2000, Neuchâtel, SwitzerlandCenter of Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile Argand 11, 2000, Neuchâtel, Switzerland; Corresponding author.Almost all Multiple-Point Statistic (MPS) methods use internally a template matching method to select patterns that best match conditioning data. The purpose of this paper is to analyze the performances of ten of the most frequently used template matching techniques in the framework of MPS algorithms. Performance is measured in terms of computing efficiency, accuracy, and memory usage. The methods were tested with both categorical and continuous training images (TI). The analysis considers the ability of those methods to locate rapidly and with minimum error a data event with a specific proportion of known pixels and a certain amount of noise.Experiments indicate that the Coarse to Fine using Entropy (CFE) method is the fastest in all configurations. Skipping methods are efficient as well. In terms of accuracy, and without noise all methods except CFE and cross-correlation (CC) perform well. CC is the least accurate in all configurations if the TI is not normalized. This method performs better when normalized training images are used. The Binary Sum of Absolute Difference is the most robust against noise. Finally, in terms of memory usage, CFE is the worst among the ten methods that were tested; the other methods are not significantly different.http://www.sciencedirect.com/science/article/pii/S2590197421000124Multiple-point statisticsTemplate matching |
| spellingShingle | Mansoureh Sharifzadeh Lari Julien Straubhaar Philippe Renard Efficiency of template matching methods for Multiple-Point Statistics simulations Applied Computing and Geosciences Multiple-point statistics Template matching |
| title | Efficiency of template matching methods for Multiple-Point Statistics simulations |
| title_full | Efficiency of template matching methods for Multiple-Point Statistics simulations |
| title_fullStr | Efficiency of template matching methods for Multiple-Point Statistics simulations |
| title_full_unstemmed | Efficiency of template matching methods for Multiple-Point Statistics simulations |
| title_short | Efficiency of template matching methods for Multiple-Point Statistics simulations |
| title_sort | efficiency of template matching methods for multiple point statistics simulations |
| topic | Multiple-point statistics Template matching |
| url | http://www.sciencedirect.com/science/article/pii/S2590197421000124 |
| work_keys_str_mv | AT mansourehsharifzadehlari efficiencyoftemplatematchingmethodsformultiplepointstatisticssimulations AT julienstraubhaar efficiencyoftemplatematchingmethodsformultiplepointstatisticssimulations AT philipperenard efficiencyoftemplatematchingmethodsformultiplepointstatisticssimulations |