Effective 3D Data Extraction for Multiple Structured Lights Projection Method

AUTHOR(S):

TITLE

Effective 3D Data Extraction for Multiple Structured Lights Projection Method

ABSTRACT

Computer vision systems have been exploited to detect the three dimensional shape data of objects. Slit-laser scanning or pattern projection on the recording process is executed on these system. Generally, since the recording process takes a time, the temporally stop of physical movement is required for both of the measurement target and the measurement device during the recording process. In order to cope with this problem, the measurement system that projected the multiple structured light is introduced. Multiple structured light projection enables the quick measurement and three-dimensional data on these structured lights can be obtained by a few images. However, in the image processing, each structured light should be separated and identified properly for estimating the three-dimensional position by the triangulation. In this paper, separation method of each structured light from the single recorded image is introduced, and this method improves the robustness of pattern recognition.

KEYWORDS

Computer vision, Point Cloud, Multiple structured lights, Laser, Three-dimensional

REFERENCES

[1] Mahoney, J., “Testing the goods: Xbox kinect. ”(2010)

[2] Bigdelou, A., Benz, T., Schwarz, L., Navab, N. “Simultaneous categorical and spatio- temporal 3d gestures using kinect. ” In: 3DUI12. , 53-60. (2012) y x z A Column with distortion B Distortion is eliminated

[3] Dal Mutto, C., Zanuttigh, P., Cortelazzo, G. “Time-of-Flight Cameras and Microsoft Kinect.”, Springer ,2012

[4] Zhang, Z. , “Microsoft kinect sensor and its effect. ”,19 , 4-10. (2012)

[5] Kapoutsis, C., Vavoulidis, C., Pitas, I. “Morphological techniques in the iterative closest point algorithm.” , I: 808-812. (1998)

[6] Sharp, G., Lee, S., Wehe, D. “Icp registration using invariant features. ”,24 ,90-102, (2002)

[7] Feldmar, J., Declerck, J., Malandain, G., Ayache, “N. Extension of the icp algorithm to nonrigid intensity-based registration of 3d volumes. ”,66 , 193-206, (1997)

[8] Lee, B., Kim, C., Park, R. “An orientation reliability matrix for the iterative closest point algorithm.”, 22 , 1205-1208, (2000)

[9] Gupta, S., Sengupta, K., Kassim, “A. Compression of dynamic 3d geometry data using iterative closest point algorithm. ”,87 , 116-130, (2002)

[10] Penney, C., Corby, N. “Coded aperture light detector for three dimensional camera.” ,(1989)

[11] Wang, Y. “Characterizing three-dimensional surface structure from visual images.”, 13 , 52- 60, (1991)

[12] Bhatnagar, D., Pujari, A., Seetharamulu, P., “Static scene analysis using structured light.”, 9 , 82-87, (1991)

[13] Aldon, M., Strauss, O. “Shape decomposition using structured light vision.”, In:VF91. , 11-20 (1991)

[14] G. Kertész, S. Szénási, Z. Vámossy, “Parallelization Methods of the Template Matching Method on Graphics Accelerators”, 16th IEEE International Symposium on Computational Intelligence and Informatics, Budapest, 2015, pp. 161-164.

Cite this paper

Kumiko Yoshida, Kikuhito Kawasue. (2016) Effective 3D Data Extraction for Multiple Structured Lights Projection Method. Computers, 1, 179-184