Paper Info
Title
Rochade: Robust Checkerboard Advanced Detection For Camera Calibration
Abstract
We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV's checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.
Year
DOI
Venue
2014
10.1007/978-3-319-10593-2_50
COMPUTER VISION - ECCV 2014, PT IV
Keywords
Field
DocType
Checkerboard Detection, Saddle-Based Subpixel Refinement, Multi Camera Calibration, Low Resolution Sensors, Lens Distortion
Distortion (optics),Perspective distortion,Computer vision,Computer science,Checkerboard,Surface fitting,Camera resectioning,Artificial intelligence,Real image,Subpixel rendering,Detector
Conference
Volume
ISSN
Citations 
8692
0302-9743
10
PageRank 
References 
Authors
0.53
12
7
Name
Order
Citations
PageRank
Simon Placht1142.35
Peter Fürsattel2100.87
Etienne Assoumou Mengue3393.48
Hannes G. Hofmann4100.53
Christian Schaller5141.00
Michael Balda6404.35
Elli Angelopoulou737534.46