Comparison of edge discrimination of microbubbles between Sobel and improved mathematics of Canny edge detection

Qaddoori, Ayman S.; Saud, Jamila H.

doi:10.22075/ijnaa.2022.5600

Comparison of edge discrimination of microbubbles between Sobel and improved mathematics of Canny edge detection

Document Type : Research Paper

Authors

Ayman S. Qaddoori ¹

Jamila H. Saud ²

¹ Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics, Baghdad, Iraq

² College of Science, Mustansiriyah University, Baghdad, Iraq

10.22075/ijnaa.2022.5600

Abstract

Bubble sizes are generated by micro-bubble generators (MBGs) in the water for their effect on the percentage of dissolved oxygen in the water and we find this in aquaculture where oxygen is important to marine life and in many applications. And since these bubbles range in size from 20 to 50, we need to highlight the shape of the bubble and distinguish it, so two Sobel algorithms were used and the Canny method was improved and compared between them, where the edge detection algorithm is sensitive to noise, and therefore, it is easy to lose weak edge information when filtering noise, and it appears ts fixed parameters are weak adaptability. In response to these problems, this paper proposed an improved algorithm based on the Canny algorithm. This algorithm introduced the concept of gravitational field strength to replace the image gradient and obtained the gravitational field strength factor. Two methods for choosing the adaptive threshold based on the average image gradation The size and standard deviation of two types of model images (one containing less edge information, the other containing rich edge information) were subtracted, respectively. The improved Canny algorithm is simple and easy to achieve. Experimental results show that the algorithm can retain more useful information and is more robust in the face of noise.

Keywords

Bubble size

Detection Technique

Sobel edge detection

improved Canny algorithm

[1] S. Agaian, A. Almuntashri and A.T. Papagiannakis, An improved Canny edge detection application for Asphalt concrete, Proc. 2009 IEEE Int. Conf. Systems, Man, and Cybernetics USA, 2009 pp.3683–3687.

[2] R. C. Bueno, P.H.F. Masotti, J.F. Justo, D.A. Andrade, M.S. Rocha, W.M. Torres and R.N.de Mesquita, Twophase flow bubble detection method applied to natural circulation system using fuzzy image processing, Nuclear Engin. Design 335 (2018) 255-264.

[3] J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Machine Intell. 8 (1986) 679–698.
[4] R. Gonzalez and R. Woods, Digital Image Processing, Addison Wesley, 1992.

[5] Li Er-sen, Zhu Shu-long, Zhu Bao-shan, Zhao Yong, Xia Chao-gui and Song Li-hua, An adaptive edge-detection method based on the Canny operator, Int. Conf. Envir. Sci. Inf. Appl. Tech. (2009) pp.465–469.

[6] C. Lopez-Molina, H. Bustince, J. Fernandez, P. Couto and B. De Baets, A gravitational approach to edge detection based on triangular norms, Pattern Recogn. 43 (2010) 3730–3741.

[7] Mitani, Yoshihiro and O. Toshitaka, Detection of microbubbles using the Hough transform, Applied Mechanics and Materials, 378 (2013).

[8] T.B. Nguyen and D. Ziou, Contextual and non-contextual performance evaluation of edge detectors, Pattern Recogn. Lett. 21 (2000) 805–816.

[9] F.A. Pellegrino, Edge Detection Revisited, IEEE Trans On System Man and Cybernetics, 34 (2004) 1500–1517.

[10] S. Sara and H. Lamela, Edge Detection of Ear Image Based on Canny Method, The 23rd Specialized Scientific Conference, collage of Education / University Mustansiriya 26-27 April 2017.

[11] S. Salman and H. Jamila, Deep learning machine using Hierarchical cluster features, Al-Mustansiriyah J. Sci. 29 (2019) 82–93.

[12] M. William, The Canny Edge Detector Revisited, Int. J. Computer Vision 91 (2011) 251–261.

[13] Y.J. Zhang, Image Engineering(II)Image Analysis, Tsinghua University Press, Beijing, 2007.

[14] Q. Zhao, R.-R. Li and Q. QU, Research on statistical detection method of microbubbles in transparent layer of quartz crucible based on image processing, J. Crystal Growth 556 (2020).