Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence
With the advancement of low-cost digital cameras and their widespread, especially in smartphones, these cameras are not designed for photogrammetry applications. The main aim of this study is to model and estimate the interior orientation parameters (IOPs) for images captured by volunteer-run camer...
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middle technical university
2023-06-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/950 |
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| author | Hasanain A. Ajjah Ahmed H H Alboabidallah Mamoun U. Mohammed Awesar A. Hussain |
| author_facet | Hasanain A. Ajjah Ahmed H H Alboabidallah Mamoun U. Mohammed Awesar A. Hussain |
| author_sort | Hasanain A. Ajjah |
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With the advancement of low-cost digital cameras and their widespread, especially in smartphones, these cameras are not designed for photogrammetry applications. The main aim of this study is to model and estimate the interior orientation parameters (IOPs) for images captured by volunteer-run cameras using artificial intelligence. These cameras were unavailable to perform traditional calibration processes or use images from unknown sources for image measuring. Estimating IOPs using random values within the range determined by testing the selected sample's consistency. Optimization was performed by Utilizing the Simulated Annealing algorithm based on stereo calibration to obtain the best possible values of these parameters that produce the minimum RMS-reprojection error attained. By The variance between the parameters from the pre-calibration process and estimated by an artificial intelligence system, The coefficient of determination in the IOPs (focal length R2 = 0.717 to 0.812, principal point (X, Y) R2 = (0.674 to 0.869, 0.504 to 0.613), Both radial and tangential coefficients, R2 was close to zero). Therefore, the estimations of radial distortions k1, k2, and tangential distortions p1 and p2 are invalid. A reasonably strong relationship between principal distance and principal point with low lens distortion parameters due to the significant relative differences between the distortion parameters, sufficient strength of the relationship between calculating parameters, and estimating according to accuracy tolerance in photogrammetry applications.
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| format | Article |
| id | doaj-art-81b8d05320624ab89914bb894a2f7bff |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-81b8d05320624ab89914bb894a2f7bff2025-01-19T11:01:52Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-06-015210.51173/jt.v5i2.950Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial IntelligenceHasanain A. Ajjah0Ahmed H H Alboabidallah1Mamoun U. Mohammed2Awesar A. Hussain3Engineering Technical College - Baghdad, Middle Technical University, Baghdad, IraqEngineering Technical College - Baghdad, Middle Technical University, Baghdad, IraqEngineering Technical College - Baghdad, Middle Technical University, Baghdad, IraqUniversity of Bradford, Bradford, BD7 1DP, UK With the advancement of low-cost digital cameras and their widespread, especially in smartphones, these cameras are not designed for photogrammetry applications. The main aim of this study is to model and estimate the interior orientation parameters (IOPs) for images captured by volunteer-run cameras using artificial intelligence. These cameras were unavailable to perform traditional calibration processes or use images from unknown sources for image measuring. Estimating IOPs using random values within the range determined by testing the selected sample's consistency. Optimization was performed by Utilizing the Simulated Annealing algorithm based on stereo calibration to obtain the best possible values of these parameters that produce the minimum RMS-reprojection error attained. By The variance between the parameters from the pre-calibration process and estimated by an artificial intelligence system, The coefficient of determination in the IOPs (focal length R2 = 0.717 to 0.812, principal point (X, Y) R2 = (0.674 to 0.869, 0.504 to 0.613), Both radial and tangential coefficients, R2 was close to zero). Therefore, the estimations of radial distortions k1, k2, and tangential distortions p1 and p2 are invalid. A reasonably strong relationship between principal distance and principal point with low lens distortion parameters due to the significant relative differences between the distortion parameters, sufficient strength of the relationship between calculating parameters, and estimating according to accuracy tolerance in photogrammetry applications. https://journal.mtu.edu.iq/index.php/MTU/article/view/950PhotogrammetryClose Range PhotogrammetryInterior OrientationArtificial Intelligence |
| spellingShingle | Hasanain A. Ajjah Ahmed H H Alboabidallah Mamoun U. Mohammed Awesar A. Hussain Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence Journal of Techniques Photogrammetry Close Range Photogrammetry Interior Orientation Artificial Intelligence |
| title | Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence |
| title_full | Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence |
| title_fullStr | Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence |
| title_full_unstemmed | Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence |
| title_short | Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence |
| title_sort | modelling and estimation of interior orientation of non metric cameras using artificial intelligence |
| topic | Photogrammetry Close Range Photogrammetry Interior Orientation Artificial Intelligence |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/950 |
| work_keys_str_mv | AT hasanainaajjah modellingandestimationofinteriororientationofnonmetriccamerasusingartificialintelligence AT ahmedhhalboabidallah modellingandestimationofinteriororientationofnonmetriccamerasusingartificialintelligence AT mamounumohammed modellingandestimationofinteriororientationofnonmetriccamerasusingartificialintelligence AT awesarahussain modellingandestimationofinteriororientationofnonmetriccamerasusingartificialintelligence |