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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ching-Han Chen Tun-Kai Yao Chia-Ming Kuo |
| Copyright Year | 2013 |
| Abstract | This study proposes an efficient back mapping model that uses a lightweight neural network and virtual calibration plate to accurately correct the distortion of low quality wide angle camera. Unlike the radial model, the neural-based method uses non-linear functional mapping to model surface distortion, which consists of wide-angle distortion and various manufacturing errors in low-cost cameras. The proposed approach uses a lightweight multilayer feed-forward neural network (MFFNN) with error back-propagation training algorithm to map the complex distortion surface. The optimal number of neurons of hidden layer was assigned as 4 for associating the mapping model between the distortion image space (DIS) with the correction image space (CIS). This study uses a 105 degree wide-angle low-cost camera to test the proposed method. Results show that the maximal corrected error in a whole image is less than 2 pixels, and that the mean square error (MSE) approaches 0.2542 between the corrected and ideal results. |
| Sponsorship | IEEE Consumer Electron, Soc, |
| Starting Page | 171 |
| Ending Page | 172 |
| File Size | 144583 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781467361989 |
| ISSN | 0747668X |
| e-ISBN | 9781467361996 |
| DOI | 10.1109/ISCE.2013.6570168 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-03 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Nonlinear distortion Cameras Lenses Optical distortion Manufacturing Adaptive optics Optical imaging |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Electrical and Electronic Engineering |
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