| 多几何特征约束的单幅图像相机自标定方法 |
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| 引用本文： | 王美珍,刘学军,卢玥,刘丹.多几何特征约束的单幅图像相机自标定方法[J].地球信息科学,2012,14(5):644-651. |
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| 作者姓名： | 王美珍 刘学军 卢玥 刘丹 |
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| 作者单位： | 南京师范大学虚拟地理环境教育部重点实验室,南京210046 |
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| 基金项目： | 国家支撑计划项目（2012BAH35B02）; 江苏省高校自然科学重大基础研究项目（10KJA420025）; 江苏省测绘科研项目（JSCHKY201204） |
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| 摘 要： | 目前,图像获取设备及方式呈现多样化趋势,获取的图像数量、重叠度等不完全具备传统摄影测量方法应用的需求.若要获取图像中包含的丰富场景几何信息,就需要发展依赖图像刻画内容的相机自标定方法,建立起图像与现实场景之间的桥梁.故此,本文提出了利用单幅图像中几何约束条件的相机自标定方法,并顾及多个几何特征约束,根据方差合理地为其设...
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| 关 键 词： | 自标定 单幅图像 多几何特征约束 不变量 几何特征 |
| 收稿时间： | 2012-04-10; |
Camera Self-calibration Using Multiple Geometric Constraints in a Single Image |
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| WANG Meizhen,LIU Xuejun,LU Yue and LIU Dan.Camera Self-calibration Using Multiple Geometric Constraints in a Single Image[J].Geo-information Science,2012,14(5):644-651. |
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| Authors: | WANG Meizhen LIU Xuejun LU Yue and LIU Dan |
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| Institution: | (Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China) |
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| Abstract: | Camera self-calibration is a key step to acquisition 3D space information from 2D image, and it is always one of the important issues in photogrammetry. However, present methods for camera self-cali- bration need two or more images and/or their corresponding points. With the development of digital de- vices for image taken and （wireless） network, a method not depending on digital device, images taken process, or multiple images, is badly needed. Consequently this paper presented a novel method that makes full use of various geometric constraints to realize reliable camera calibration for a single image. Firstly, this paper summarized various geometric constraints and invariants for the existing camera self- calibration method. Secondly, in order to build the relationships among geometric constraints for calibra- tion, we coded for different planes and geometric features in an image. Because variance represents the er- ror distribution, it can be considered as the determinant. In this paper, we obtained the variance of differ- ent combination of geometric features for camera calibration by means of fitting each groups of geometric features for thirty times, and then depended on the variance above to determine the weight of each camera ＂s internal parameters. Finally, based on each camera＇s internal parameters, here we only focus on loci length, and their corresponding weights, the ultimate results are computed. Two images which depict in- side and outside scene respectively were chosen to test the usability of our methods. In order to avoid the influence of image distortion, we corrected it using amethod we proposed in another paper before tests. The test results show that： 1） the weighted method gave a more stable result, relative to the result of each group geometric constraints, that is one group＇s relative error is two high and in other may be lower; 2） the weighted method obtained a higher accuracy result than the mean of all groups. The results of veri- fication testing for the two images of the indicated that our weighted method can comprehensive employs variety of geometric constraints in single image, in the other side, it also takes their corresponding vari- ance into account. It makes full use of the variety, usability and stability of geometric constraints. It can be employed to images depict indoor and outdoor which contains more geometric constraints. |
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| Keywords: | self-calibration single image multiple geometric constraints invariant geometric constraints |
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