Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks
1
Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea
2
Future Vehicle Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea
3
Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 894; https://doi.org/10.3390/s20030894
Received: 16 January 2020
/
Revised: 30 January 2020
/
Accepted: 4 February 2020
/
Published: 7 February 2020
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches.
View Full-Text
Keywords:
automatic distortion rectification; wide-angle lens; fish-eye lens; advanced driver-assistance system (ADAS); video-surveillance; vision tasks
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Kakani, V.; Kim, H.; Lee, J.; Ryu, C.; Kumbham, M. Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks. Sensors 2020, 20, 894. https://doi.org/10.3390/s20030894
AMA Style
Kakani V, Kim H, Lee J, Ryu C, Kumbham M. Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks. Sensors. 2020; 20(3):894. https://doi.org/10.3390/s20030894
Chicago/Turabian StyleKakani, Vijay, Hakil Kim, Jongseo Lee, Choonwoo Ryu, and Mahendar Kumbham. 2020. "Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks" Sensors 20, no. 3: 894. https://doi.org/10.3390/s20030894
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
