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Alternative TitleApplication of Weighted Least Squares Technique in Computer Vision
Thesis Advisor吴福朝
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword摄像机标定 摄像机位姿估计 一维标定 平面标定 加权最小二乘 Camera Calibration Camera Pose Estimation 1d Calibration 2d Calibration Weighted Least Squares
Abstract摄像机标定和姿态估计是计算机视觉技术研究的基本问题。本文旨在应用加权最小二乘技术探索新的标定和姿态估计算法,主要工作归纳如下: 1. 针对摄像机位姿问题提出了一种加权线性方法,其关键思想是通过加权使经典线性方法的代数误差近似于重投影算法的几何误差, 从而达到接近于最大似然估计的精度。通过对DLT和EPnP使用加权的方法,给出了加权DLT算法(WDLT)和加权EPnP算法(WEPnP)。大量模拟数据和真实图像实验均表明,WDLT和WEPnP不仅能提高DLT和EPnP的精度,并且在深度比较小的情况下优于Lu的非线性算法。 2. 通过对经典1D标定算法的噪声模型分析,提出了一种高精度的加权线性1D标定算法。因1D标定算法估计精度依赖于深度估计,本文提出的算法的基本思想是利用深度估计误差和图像点的欧氏距离关系,使用图像距离作为权值的加权最小二乘算法代替经典的最小二乘算法。大量实验表明,本方法和经典算法有相同的计算复杂度,但极大地提高了标定精度。
Other AbstractCamera calibration and pose determination are two fundamental problems in computer vision. The thesis aims to explore new algorithms for the camera calibration and pose estimation using weighted least squares technique. The main points are summarized as follows: 1. A novel weighted linear method for the camera pose estimation is presented. The key idea of this method is to replace the algebraic error in the classic linear method with the weighted algebraic error to adequately approximate the geometric error. The method provides a linear solution whose accuracy is close to the accuracy of ML estimation. Based on the DLT and EPnP, the weighted DLT (WDLT) and weighted EPnP (WEPnP) are proposed. Experiments with simulative data and real images show that the WDLT and WEPnP remarkably outperform the DLT and EPnP and in the case of small depth ratio, both of them also outperform the Lu’s nonlinear algorithm. 2. A high-precision weighted linear 1D calibration algorithm is proposed. Since the accuracy of 1D calibration depends on the estimation of depth, based on the relationship between errors of depth and image distance, the distance between image points is used as the weight for each linear constraint in the classical 1D calibration, and the 1D calibration is transformed into a weighted linear least squares problem. A large number of experiments show that, with a comparable computational complexity, the calibration accuracy of our weighted algorithm is substantially improved.
Other Identifier200728014628041
Document Type学位论文
Recommended Citation
GB/T 7714
杨森. 加权最小二乘技术在计算机视觉中的应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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