Experimental Validation of Bias in Checkerboard Corner Detection
- Resource Type
- Conference
- Authors
- Edwards, Matthew J.; Hayes, Michael P.; Green, Richard D.
- Source
- 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Image and Vision Computing New Zealand (IVCNZ), 2020 35th International Conference on. :1-6 Nov, 2020
- Subject
- Computing and Processing
Signal Processing and Analysis
Image edge detection
Distortion
Cameras
Approximation algorithms
Rendering (computer graphics)
Standards
Lenses
Machine vision
corner detection
statistical analysis
- Language
- ISSN
- 2151-2205
The sub-pixel corner refinement algorithm in OpenCV is widely used to refine checkerboard corner location estimates to sub-pixel precision. This paper shows using both simulations and a large dataset of real images that the algorithm produces estimates with significant bias and noise which depend on the sub-pixel corner location. In the real images, the noise ranged from around 0.013 px at the pixel centre to 0.0072 px at the edges, a difference of around $1.8\times$. The bias could not be determined from the real images due to residual lens distortion; in the simulated images it had a maximum magnitude of 0.043 px.