Segmentation of Gray Scale Images of Dropwise Condensation on Textured Surfaces

Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

Abstract:

In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: Dropwise condensation, textured surface, image processing, watershed.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474614

References:

[1] R. N. Leach, F. Stevens, and J. T. Dickinson. Dropwise condensation - experiments and simulations of nucleation and growth of water drops in a cooling system. Technical report, Physics Department, Washington State University, 2006.
[2] Y. A. Cengel. Heat transfer - a practical approach. Technical report, International Edition, 1998.
[3] Julian E. Castillo, Justin A. Weibel, and Suresh V. Garimella. The effect of relative humidity on dropwise condensation dynamics. Technical report, Cooling Technologies Research Center, Purdue University, 2015.
[4] R. N. Leach, F. Stevens, S. C. Langford, and J. T. Dickinson. Dropwise condensation: eperiments and simulations of nucleation and growth of water drops in alccoling system. Technical report, Physics Department, Washington State University, 2009.
[5] J. W. Rose and L. R. Glicksman. Dropwise condensation - the distribution of drop sizes. Technical report, Department of Mechanical Engineering, Massachusets Institute of Technology, 1972.
[6] Basant Singh Sikarwar, Sameer Khandekar, and K. Muralidhar. Mathematical modelling of dropwise condensation on textured surfaces. Technical report, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 2012.
[7] Yucheng Fu and Yang Liu. Development of a robust image processing technique for bubbly flow measurement in a narrow rectangular channel. Technical report, Nuclear Engineering Program, Mechanical Engineering Department, Virginia Tech, 2016.
[8] Soo-Chang Pei and Ji-Hwei Horng. Circular arc detection based on hough transform. Technical report, Department of Electrical Engineering, National Tawan University, 1995.
[9] Xiaoran Yu, Dongchang Xing, Tatsuya Hazuku, Tomoji Takamasa, Takaski Ishimaru, Yuji Tanaka, and Tatsuro Akiba. Measurement technique for solid-liquid two-phase flow using normal-line hough transform method. Technical report, Tokyo University of Marine Science and Technology - Research Institute for Cell Engineering Advanced Industrial Science and Technology, 2009.
[10] T. J. Atherthon and D. J. Kerbyson. Using phase to represent radius in the coherent circle hough transform. Technical report, Department of Computer Science, University of Warwick, 1993.
[11] R. Muthukrishnan and M. Radha. Edge detection technique for image segmentation. Technical report, Department of Statistics, Bharathiar University, 2011.
[12] Liping Shen, Xiangqun Song, Manabu Iguchi, and Fujio Yamamoto. A method for recognizing particles in overlapped particle images. Technical report, Department of Mechanical Engineering, Fukui University - Division of Materials Science and Engineering, Graduate School of Engineering, Hokkado University, 1999.
[13] M. Honkanen. Turbulent multiphase flow measurements with particle image velocimetry: application to bubbly flows. Technical report, Tampere University of Technology, 2002.
[14] Herbert Freeman and Larry S. Davis. A corner-finding algorithm for chain-coded curves. Technical report, IEEE Transactions on Computers, 1977.
[15] C. Urdiales, A. Bandera, and F. Sandoval. Non-parametric planar shape representation based on adaptative curvature functions. Technical report, Dpto Technologica Electronica ETSI Telecommunicacion, Universidad de Malaga, 2002.
[16] R. Lindken and W. Merzkirch. A novel piv technique for measurements in multiphase flows and its application to two-phase bubbly flows. Technical report, DHochschule Bochum, Mecatronics and Mechanical Eengineering Department, 2002.
[17] S. Beucher and F. Meyer. The morphological approach to segmentation; the watershed transformation. Technical report, Centre de morphologie mathematique, Ecole des Mines de Paris, 1990.
[18] X. Zabulis, M. Papara, A. Chatziargyriou, and T. D. Karapantsios. Detection of densely dispersed spherical bubbles in digital images based in template matching technique - application to wet foams. Technical report, Division of Chemcal Technology, School of Chemistry, Aristotelian University of Thessaloniki - University Box Informatics and Telematics Institute - Centre for Research and Technology Hellas, 2007.
[19] Andrew W. Fitzgibbon, Maurizio Pilu, and Robert B Fisher. Direct least-squares fitting of ellipses. Technical report, Department of Artificial Intelligence, the University of Edinburgh, 1996.
[20] Tomislav Marosevic and Rudolf Scitovski. Multiple ellipse fitting by center-based clustering. Technical report, Multiple ellipse fitting by center-based clustering, 2015.
[21] A. La Torre, L. Alonso-Nanclares, S. Muelas, J. M. Pena, and J. DeFelipe. Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images. Technical report, Instituto Cajal, Consejo Superior de Investigaciones - Larboratorio Cajal de Circuitos Corticales and Facultad de Informatica, Universidad Politecnica de Madrid, 2013.
[22] W. X. Wang. Binary image segmentation of aggregates based on polygonal approximation and classification of concavities. Technical report, Dpto Technologica Electronica ETSI Telecommunicacion, Universidad de MalagaDivision of Engineering Geology, Department of Civil and Environmental Engineering, Royal Institute of Technology, 1997.
[23] D. Thompson, H. G. J. W. Haddad, and P. H. Bartels. Scene segmentation in a machine vision system for histopathology. Technical report, Optical Science Center, University of Arizona, 1990.
[24] S. H. Ong and Jayasooriah. Decomposition of digital clumps into convex parts by contour tracing and labelling. Technical report, Optical Science Center, University of ArizonaDepartment of Electrical Engineering, National University of Singapore, 1992.
[25] Ashish Karn, Christopher Ellis, Roger Arndt, and Jiarong Hong. An integrative image measurement technique for dense bubbly flows with a wide size distribution. Technical report, Saint Anthony Falls Laboratory, University of Minnesota, 2015.
[26] Qing Chen, Xiaoli Yang, and Emil M Petrui. Watershed segmentation for binary images with different distance transforms. Technical report, University of Ottawa, Lakehead University, 2004.
[27] R. M. Haralick and L. Waston. A facet model for image data. Technical report, Computer Vision Graphics Image Process, 1981.
[28] Timo Ojala, Matti Pietikinen, and David Harwood. Performance evaluation of texture measures with classification based on kullback discrimination of distributions. Technical report, Department of Electrical Engineering, University of Ouu - Center for Automation Researc, University of Maryland, 1994.
[29] K. L. Lee and L. H. Chen. A new method for coarse classification of textures and class weight estimation for texture retrieva. Technical report, Department of Computer and Information Science, National Chiao Tung University, 2002.
[30] Tom Fawcett. An introduction to roc analysis. Technical report, Institute for the Study of Learning and Expertise, 2005.