Calibration of an intensity ratio system for 3D imaging

H. T. Tsui; K. C. Tang

doi:10.1117/12.949043

7 March 1989 Calibration of an intensity ratio system for 3D imaging

H. T. Tsui, K. C. Tang

Proceedings Volume 1005, Optics, Illumination, and Image Sensing for Machine Vision III; (1989) https://doi.org/10.1117/12.949043
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

Abstract

An intensity ratio method for 3D imaging is proposed with error analysis given for assessment and future improvements. The method is cheap and reasonably fast as it requires no mechanical scanning or laborious correspondence computation. One drawback of the intensity ratio methods which hamper their widespread use is the undesirable change of image intensity. This is usually caused by the difference in reflection from different parts of an object surface and the automatic iris or gain control of the camera. In our method, gray-level patterns used include an uniform pattern, a staircase pattern and a sawtooth pattern to make the system more robust against errors in intensity ratio. 3D information of the surface points of an object can be derived from the intensity ratios of the images by triangulation. A reference back plane is put behind the object to monitor the change in image intensity. Errors due to camera calibration, projector calibration, variations in intensity, imperfection of the slides etc. are analyzed. Early experiments of the system using a newvicon CCTV camera with back plane intensity correction gives a mean-square range error of about 0.5 percent. Extensive analysis of various errors is expected to yield methods for improving the accuracy.

Citation Download Citation

H. T. Tsui and K. C. Tang "Calibration of an intensity ratio system for 3D imaging", Proc. SPIE 1005, Optics, Illumination, and Image Sensing for Machine Vision III, (7 March 1989); https://doi.org/10.1117/12.949043

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KEYWORDS

Projection systems

Cameras

Imaging systems

Error analysis

Calibration

Machine vision

Light sources

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