Background elimination technique in the structured light system for dynamic environment

The utilization of structured light system is increasing not only in industrial areas but also in dynamic environment including the motion recognition for Xbox 360 based on the motion sensor kinect and the obstruction recognition for mobile robot navigation. In this paper, we presented a method to estimate the background at t2 (projector on) by using the background images at t3 (projector off) and t1 (projector off) in the dynamic environment where there exists a motion of structured light system or an object. Differentiation of the estimated background image from the image at t2 where the signal and the background are mixed can not only increase the signal-to-noise ratio between the projected pattern and the noise but also eliminate the noise similar to the signal that is detected in the background range. The experimental result not only provided the three-dimensional data from which the actual noise range was removed but also suggested the correlation between the size of interpolation area and the estimated error.

[1]  André Oosterlinck,et al.  Range Image Acquisition with a Single Binary-Encoded Light Pattern , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[3]  Joaquim Salvi,et al.  Pattern codification strategies in structured light systems , 2004, Pattern Recognit..

[4]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[5]  Georg Wiora High-resolution measurement of phase-shift amplitude and numeric object phase calculation , 2000, SPIE Optics + Photonics.

[6]  Jens Guehring,et al.  Dense 3D surface acquisition by structured light using off-the-shelf components , 2000, IS&T/SPIE Electronic Imaging.

[7]  Sara Lazzari,et al.  3D imager for dimensional gauging of industrial workpieces: state of the art of the development of a robust and versatile system , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[8]  Dirk Bergmann,et al.  New approach for automatic surface reconstruction with coded light , 1995, Optics & Photonics.

[9]  Li Zhang,et al.  Rapid shape acquisition using color structured light and multi-pass dynamic programming , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[10]  François Blais Review of 20 years of range sensor development , 2004, J. Electronic Imaging.

[11]  T. Kanade,et al.  A Method of Time-Coded Parallel Planes of Light for Depth Measurement , 1981 .

[12]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[13]  Shojiro Sakata,et al.  Reconstruction Of Surfaces Of 3-D Objects By M-array Pattern Projection Method , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[14]  P. Venkataraman,et al.  Applied Optimization with MATLAB Programming , 2001 .

[15]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .