Water Film Detection in Water Distribution Test of PCCS

Article Preview

Abstract:

In the passive containment cooling system (PCCS), water distribution tests are essential to verify the water distribution devices performance. Without regular boundaries and homogeneous intensities, images of water film acquired from these tests are hard to be detected by conventional approaches. We propose an improved segmentation method to identify the water film areas from the complex background. Considering the gray distortion resulted from asymmetric illumination, the method combines the modified motion segmentation and optimal threshold method. Detection results show that this method is hardly affected by the illumination change, and also insensitive to noise.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

663-667

Citation:

Online since:

August 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Lawrence E Conway, William A Stewart, Passive Containment Cooling System, U.S., 5, 049, 353[P]. Sep, 17, (1991).

Google Scholar

[2] Massimo Piccardi, Background subtraction techniques: a review, in Proceedings of IEEE Conference on Systems, Man and Cybernetics (IEEE, 2004), p.3099–3104 vol. 4.

DOI: 10.1109/icsmc.2004.1400815

Google Scholar

[3] P. L. Rosin and T. Ellis, Image difference threshold strategies and shadow detection, in Proceedings of the 6th British Machine Vision Conference, pp.347-356, Birmingham, Ala, USA, September (1995).

DOI: 10.5244/c.9.35

Google Scholar

[4] A. Elgammal, D. Harwood, and L. S. Davis, Non-parametric Model for Background Subtraction, in Proceedings of the 6th European Conference on Computer Vision, pp.751-767, Dublin, Ireland, June (2000).

DOI: 10.1007/3-540-45053-x_48

Google Scholar

[5] Jwu-Sheng Hu and Tzung-Min Su, Robust background subtraction with shadow and highlight removal for indoor surveillance, EURASIP Journal on Applied Signal Processing. vol. 1, 1 January 2007, pp.108-108.

DOI: 10.1155/2007/82931

Google Scholar

[6] Robert M. Haralick, Linda G. Shapiro, Image segementation techniques, Computer Vision, Graphics, and Image Processing. Volume 29, Issue 1, January 1985, Pages 100-132.

DOI: 10.1016/s0734-189x(85)90153-7

Google Scholar

[7] Zhang Yujin, Image Engineering (Ⅱ), Image Analysis (Second Edition), (2005).

Google Scholar

[8] Nobuyuki OTSU, A threshold selection method from gray level histogram, IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-9, NO. 1, January 1979. pp.62-66.

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[9] Ren Bin, Wang Bingquan, Luo Bin, The method of threshold and peak detection on histogram exponent smoothing, China Journal of Image and Graphics. Vol. 2, No. 4, Apr, 1997. pp.230-233.

Google Scholar

[10] M. Ibrahim Sezan, A peak detection Algorithm and its application to histogram-based image data reduction, Computer Vision, Graphics, and Image Processing. Volume 49, Issue 1, January 1990, Pages 36-51.

DOI: 10.1016/0734-189x(90)90161-n

Google Scholar