posted on 2016-09-21, 08:50authored byRene Wackrow, Edgar Ferreira, Jim Chandler, Koji Shiono
Imaging systems have an indisputable role in revealing vegetation posture under diverse flow conditions, image sequences being generated with off the shelf digital cameras. Such sensors are cheap but introduce a range of distortion effects, a trait only marginally tackled in hydraulic studies focusing on water-vegetation dependencies. This paper aims to bridge this gap by presenting a simple calibration method to remove both camera lens distortion and refractive effects of water. The effectiveness of the method is illustrated using the variable projected area, computed for both simple and complex shaped objects. Results demonstrate the significance of correcting images using a combined lens distortion and refraction model, prior to determining projected areas and further data analysis. Use of this technique is expected to increase data reliability for future work on vegetated channels.
History
School
Architecture, Building and Civil Engineering
Published in
Sensors and Techniques for 3D Object Modeling in Underwater Environments
Pages
68 - 79
Citation
WACKROW, R. ... et al, 2016. Geometric modeling and photogrammetric camera calibration: camera calibration for water-biota
research: the projected area of vegetation. IN: Menna, F., Remondino, F. and
Maas, H.-G. (eds). Sensors and Techniques for 3D Object Modeling in Underwater Environments. Shu-Kun Lin, pp. 68-79.
Publisher
Shu-Kun Lin
Version
VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2016
Notes
Reprinted from Sensors. Cite as: Wackrow, R.; Ferreira, E.; Chandler, J.; Shiono, K.
Camera Calibration for Water-Biota Research: The Projected Area of Vegetation. This is an open access article distributed under the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sensors 2015, 15, 30261–30269.