Matrix analysis
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of Camera Location from 2-D to 3-D Line and Point Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
International Journal of Computer Vision
Geometric computation for machine vision
Geometric computation for machine vision
Robust methods for estimating pose and a sensitivity analysis
CVGIP: Image Understanding
Error propagation in machine vision
Machine Vision and Applications
Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
An Industrial Augmented Reality Solution For Discrepancy Check
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Computers and Electrical Engineering
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To develop a reliable computer vision system, the employed algorithm must guarantee good output quality. In this study, to ensure the quality of the pose estimated from line features, two simple test functions based on statistical hypothesis testing are defined. First, an error function based on the relation between the line features and some quality thresholds is defined. By using the first test function defined by a lower bound of the error function, poor input can be detected before estimating the pose. After pose estimation, the second test function can be used to decide if the estimated result is sufficiently accurate. Experimental results show that the first test function can detect input with low qualities or erroneous line correspondences and that the overall proposed method yields reliable estimated results.