NDLI: Probabilistic approach for maximum likelihood estimation of pose using lines

Content Provider	IET Digital Library
Author	Zhang, Yueqiang Li, Xin Liu, Haibo Shang, Yang
Abstract	In this study, the authors have proposed a new solution for the problem of pose estimation from a set of matched 3D model and 2D image lines. Traditional line-based pose estimation methods utilising the finite information of the observations are based on the assumption that the noises for the two endpoints of the image line segment are statistically independent. However, in this study, the authors prove that these two noises are negatively correlative when the image line segment is fitted by the least-squares technique from the noisy edge points. Moreover, the authors derive the noise model describing the probabilistic relationship between the 3D model line and their finite image observations. Based on the proposed noise model, the maximum-likelihood approach is exploited to estimate the pose parameters. The authors have carried out synthetic experiments to compare the proposed method to other pose optimisation methods in the literature. The experimental results show that the proposed methods yield a clear higher precision than the traditional methods. The authors also use real image sequences to demonstrate the performance of the proposed method.
Starting Page	475
Ending Page	482
Page Count	8
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 6, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0099
Journal	IET Computer Vision
Publisher Date	2016-03-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	2D Image Lines 3D Model Computer Vision And Image Processing Technique Image Recognition Image Segmentation Image Sequence Interpolation And Function Approximation Least Squares Approximation Least-squares Technique Maximum Likelihood Estimation Maximum Likelihood Pose Estimation Numerical Analysis Pose Estimation Probabilistic Approach Probability Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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