Random three-step phase retrieval approach based on difference map Gram–Schmidt orthonormalization and Lissajous ellipse fitting method

2019 ◽  
Vol 121 ◽  
pp. 11-17 ◽  
Author(s):  
Yu Zhang ◽  
Xiaobo Tian ◽  
Rongguang Liang
Keyword(s):  
Phase Retrieval ◽  
Ellipse Fitting ◽  
Difference Map ◽  
Fitting Method

Phase retrieval based on difference map and deep neural networks

2021 ◽  
pp. 1-13
Author(s):  
Baopeng Li ◽  
Okan K. Ersoy ◽  
Caiwen Ma ◽  
Zhibin Pan ◽  
Wansha Wen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Phase Retrieval ◽  
Deep Neural Networks ◽  
Difference Map

Building an accurate 3D model of a circular feature for robot vision

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
L. Li
Keyword(s):  
Efficient Method ◽  
Error Compensation ◽  
3D Model ◽  
Robot Vision ◽  
Model Analysis ◽  
Perspective Projection ◽  
Ellipse Fitting ◽  
Projection Scheme ◽  
Fitting Method ◽  
Data Points

AbstractIn this paper, an accurate 3D model analysis of a circular feature is built with error compensation for robot vision. We propose an efficient method of fitting ellipses to data points by minimizing the algebraic distance subject to the constraint that a conic should be an ellipse and solving the ellipse parameters through a direct ellipse fitting method by analysing the 3D geometrical representation in a perspective projection scheme, the 3D position of a circular feature with known radius can be obtained. A set of identical circles, machined on a calibration board whose centres were known, was calibrated with a camera and did the model analysis that our method developed. Experimental results show that our method is more accurate than other methods.

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