SEDL / Journals / Geotechnical Testing Journal (GTJ) / Citation Page


Volume 31, Issue 2 (March 2008)

ISSN: 1945-7545
CODEN: GTJODJ
Published Online: 1 October 2007
Page Count: 6


Camera Calibration Using Neural Network for Image-Based Soil Deformation Measurement Systems

Zhao, Honghua
Civil, Architectural, and Environmental Engineering, University of Missouri-Rolla,

Ge, Louis
Civil, Architectural, and Environmental Engineering, University of Missouri-Rolla,

(Received 16 July 2006; accepted 3 August 2007)

Abstract

A neural network camera calibration algorithm has been adapted for image-based soil deformation measurement systems. This calibration algorithm provides a highly accurate prediction of object data points from their corresponding image points. The experimental setup for this camera calibration algorithm is rather easy, and can be integrated into particle image velocimetry (PIV) to obtain the full-field deformation of a soil model. The performance of this image-based measurement system was illustrated with a small-scale rectangular footing model. This fast and accurate calibration method will greatly facilitate the application of an image-based measurement system into geotechnical experiments.



Keywords:
neural network, camera calibration, particle image velocimetry

Paper ID: GTJ100729
DOI: 10.1520/GTJ100729
ASTM International is a member of CrossRef.

Author Title Camera Calibration Using Neural Network for Image-Based Soil Deformation Measurement Systems Symposium , 0000-00-00 Committee D18