Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm
Although several algorithms have been presented to solve the simultaneous pose and correspondence estimation problem, the correct solution may not be reached to with the traditional random-start initialization method. In this paper, we derive a novel method which estimates the initial value based on...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/828241 |
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| _version_ | 1832547259552628736 |
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| author | Haiwei Yang Fei Wang Zhe Li Hang Dong |
| author_facet | Haiwei Yang Fei Wang Zhe Li Hang Dong |
| author_sort | Haiwei Yang |
| collection | DOAJ |
| description | Although several algorithms have been presented to solve the simultaneous pose and correspondence estimation problem, the correct solution may not be reached to with the traditional random-start initialization method. In this paper, we derive a novel method which estimates the initial value based on genetic algorithm, considering the influences of different initial guesses comprehensively. First, a set of random initial guesses is generated as candidate solutions. Second, the assignment matrix and the perspective projection error are computed for each candidate solution. And then each individual is modified (selection, crossover, and mutation) in current iterative process. Finally, the fittest individual is stochastically selected from the final population. With the presented initialization method, the proper initial guess could be first calculated and then the simultaneous pose and correspondence estimation problem could be solved easily. Simulation results with synthetic data and experiments on real images prove the effectiveness and robustness of our proposed method. |
| format | Article |
| id | doaj-art-f32e3c0b0ca6454cb895c7b53c308f09 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-f32e3c0b0ca6454cb895c7b53c308f092025-02-03T06:45:31ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/828241828241Simultaneous Pose and Correspondence Estimation Based on Genetic AlgorithmHaiwei Yang0Fei Wang1Zhe Li2Hang Dong3 Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, China Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, China Xi'an Institute of Optics and Precision Mechanics, CAS, Xi'an, Shaanxi, China Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, ChinaAlthough several algorithms have been presented to solve the simultaneous pose and correspondence estimation problem, the correct solution may not be reached to with the traditional random-start initialization method. In this paper, we derive a novel method which estimates the initial value based on genetic algorithm, considering the influences of different initial guesses comprehensively. First, a set of random initial guesses is generated as candidate solutions. Second, the assignment matrix and the perspective projection error are computed for each candidate solution. And then each individual is modified (selection, crossover, and mutation) in current iterative process. Finally, the fittest individual is stochastically selected from the final population. With the presented initialization method, the proper initial guess could be first calculated and then the simultaneous pose and correspondence estimation problem could be solved easily. Simulation results with synthetic data and experiments on real images prove the effectiveness and robustness of our proposed method.https://doi.org/10.1155/2015/828241 |
| spellingShingle | Haiwei Yang Fei Wang Zhe Li Hang Dong Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm International Journal of Distributed Sensor Networks |
| title | Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm |
| title_full | Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm |
| title_fullStr | Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm |
| title_full_unstemmed | Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm |
| title_short | Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm |
| title_sort | simultaneous pose and correspondence estimation based on genetic algorithm |
| url | https://doi.org/10.1155/2015/828241 |
| work_keys_str_mv | AT haiweiyang simultaneousposeandcorrespondenceestimationbasedongeneticalgorithm AT feiwang simultaneousposeandcorrespondenceestimationbasedongeneticalgorithm AT zheli simultaneousposeandcorrespondenceestimationbasedongeneticalgorithm AT hangdong simultaneousposeandcorrespondenceestimationbasedongeneticalgorithm |