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دسته بندی:
حقوق خصوصی - Private law
سال انتشار:
2020
عنوان انگلیسی مقاله:
Camera calibration from very few images based on soft constraint optimization
ترجمه فارسی عنوان مقاله:
کالیبراسیون دوربین از تصاویر بسیار معدود بر اساس بهینه سازی محدودیت نرم
منبع:
Sciencedirect - Elsevier - Journal of the Franklin Institute, 357 (2020) 2561-2584. doi:10.1016/j.jfranklin.2020.02.006
نویسنده:
Hongjun Zhu a , b , d , 1 , ∗, Yan Li b , 1 , Xin Liu a , d , Xuehui Yin a , d , Yanhua Shao c , Ying Qian a , d , ∗, Jindong Tan
چکیده انگلیسی:
Camera calibration is a basic and crucial problem in photogrammetry and computer vision. Although
existing calibration techniques exhibit excellent precision and flexibility in classical cases, most of them
need from 5 to 10 calibration images. Unfortunately, only a limited number of calibration images and
control points can be available in many application fields such as criminal investigation, industrial
robot and augmented reality. For these cases, this paper presented a two-step calibration based on
soft constraint optimization, which is motivated by "no free lunch" theorem and error analysis. The key
steps include (1) homography estimation with weighting function, (2) Initialization based on a simplified
model, and (3) soft constraint optimization in terms of reprojection error. The proposed method provides
direct access to geometric information of the object from very few images. After extensive experiments,
the results demonstrate that the proposed algorithm outperforms Zhang’s algorithms from the point of
view of the success ratio, accuracy and precision.
قیمت: رایگان
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