Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise
DOI:
https://doi.org/10.35877/454RI.jinav1564Keywords:
Sand Soil Imagery, Gaussian Noise, Image Processing, Otsu Thresholding, Watershed Transform.Abstract
Image processing technology is one of the technologies that can help facilitate and speed up human work, especially in the process of determining the grain size distribution of soil in a civil building plan. Its utilization has been widely used to study, analyze, and understand the structure and framework of the soil. Image analysis is carried out as an initial or fundamental step in image processing to discover and comprehend information. With so many image segmentation methods, it is necessary to conduct research to determine which method is best for sandy soil image segmentation based on one of the image segmentation quality criteria, namely gaussian image noise. By testing the watershed transform method and the Otsu thresholding method as two of the area-based methods that are considered suitable for segmenting sandy soil images before and after distorted Gaussian noise based on the calculation of the mean square error (MSE) value.The results showed that the watershed transform method is better for segmenting sandy soil images when compared to the Otsu thresholding method. This is indicated by the average squared error (mse) of 3.08 for the watershed transform method and 4.09 for the Otsu Thresholding method. In addition to the comparison of quality tests of sandy soil based on gaussian noise with standard deviation values of normal distribution and noise intensities of 10, 20, and 30, it proves that the watershed transform method is still better at segmenting noise-distorted sandy soil images than the Otsu thresholding method. However, in terms of processing time, the Otsu Thresholding method is faster or better than the Watershed Transform method. of the results or conclusions brief. There are no citations, tables or figures in abstract.
References
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[48] B. E. Romero-Tarazona, C. L. Rodriguez-Sandoval, J. G. Villabonai-Ascanio, and A. D. Rincón-Quintero, “Development of an artificial vision system that allows non-destructive testing on flat concrete slabs for surface crack detection by processing of digital images in MATLAB,” IOP Conf. Ser. Mater. Sci. Eng., vol. 844, no. 1, 2020.
[49] J. Li, R. Zhang, J. Li, Z. Wang, H. Zhang, and B. Zhan, “Postharvest Biology and Technology Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method,” Postharvest Biol. Technol., vol. 158, no. August, p. 110986, 2019.
[50] B. S. Rao, “Dynamic Histogram Equalization for contrast enhancement for digital images,” Appl. Soft Comput. J., vol. 89, p. 106114, 2020.
[51] B. Ye, H. Hu, X. Bao, and P. Lu, “Reliquefaction behavior of sand and its mesoscopic mechanism,” Soil Dyn. Earthq. Eng., vol. 114, no. March, pp. 12–21, 2018.
[52] G. Fico, J. Montalva, A. Medrano, N. Liappas, G. Cea, and M. T. Arredondo, “EMBEC & NBC 2017,” IFMBE Proc., vol. 65, pp. 1089–1090, 2018.
[53] O. Joshua, T. Ibiyemi, and B. Adu, “A Comprehensive Review On Various Types of Noise in Image Processing,” Int. J. Sci. Eng. Res., vol. 10, no. November, pp. 388–393, 2019.
[54] Y. Aimin, L. Shanshan, L. Honglei, and J. Donghao, “Edge extraction of mineralogical phase based on fractal theory,” Chaos, Solitons and Fractals, vol. 117, pp. 215–221, 2018.
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Published
2022-07-31
How to Cite
Tomasila, G. (2022). Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise. JINAV: Journal of Information and Visualization, 3(1), 81–92. https://doi.org/10.35877/454RI.jinav1564
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