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

This paper describes a neural network camera calibration algorithm adapted for image-based soil deformation measurement systems. This calibration algorithm gives 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 to obtain the full-field deformation of a soil model. The performance of this image-based measurement system is 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.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01496115
• Authors:
  • Zhao, Honghua
  • Ge, Louis
• Publication Date: 2008-3

Language

• English

Media Info

• Media Type: Print
• Features: Figures; Photos; References; Tables;
• Pagination: pp 192-197
• Serial:
  • Geotechnical Testing Journal
  • Volume: 31
  • Issue Number: 2
  • Publisher: ASTM International
  • ISSN: 0149-6115
  • EISSN: 1945-7545
  • Serial URL: https://www.astm.org/products-services/standards-and-publications/geotechnical-testing-journal.html

Subject/Index Terms

• TRT Terms: Algorithms; Calibration; Cameras; Deformation; Geotechnical engineering; Image analysis; Image processing; Neural networks; Soil science
• Subject Areas: Geotechnology; Highways; I42: Soil Mechanics;

Filing Info

• Accession Number: 01100305
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 3 2008 7:22AM