CLOUDINESS IMAGES MULTILEVEL SEGMENTATION BY PIECEWISE LINEAR APPROXIMATION OF CUMULATIVE HISTOGRAM
DOI:
https://doi.org/10.47839/ijc.19.2.1762Keywords:
approximation, threshold, piecewise-linear function, cumulative histogram.Abstract
The Ramer-Douglas-Peucker algorithm for piecewise approximation is used for image multilevel segmentation. The cumulative histogram is selected as a function for approximation. The algorithm allows you to determine threshold values of continuous and discrete images. The algorithm is used to separate cloudiness from background and to calculate cloudiness intensity. The found points of the approximated function have been accepted to change pixel intensity by proposed formulas. The algorithm efficiency is compared with those based on ordinary and cumulative histograms. By controlling the number of points for piecewise linear approximation function, the necessary segmentation accuracy can be achieved. The algorithm complexity is linear to the number of image pixels and to the number of intensity steps. The developed algorithm is applied to the satellite map images to separate clouds of different intensity. The extracted clouds of different intensity are used to classify regions by cloudiness with a developed clustering algorithm. Testing and experimental results are presented.References
N. Otsu, “A threshold selection method from gray level histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, issue 1, pp. 62–66, 1979.
A. Rosenfeld, P. De La Torre, “Histogram concavity analysis as an aid in threshold selection,” IEEE Transactions on Systems, Man and Cybernetics, vol. 13, issue 2, pp. 231–235, 1983.
W. Tsai, “Moment-preserving thresholding: a new approach,” Computer Vision, Graphics & Image Processing, vol. 29, no. 3, pp. 377–393, 1985.
J.N. Kapur, P.K. Sahoo, A.K.C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer Vision, Graphics & Image Processing, vol. 29, no. 3, pp. 273–285, 1985.
P.L. Rosin, L. Paul, “Unimodal thresholding,” Pattern Recognition, vol. 34, no. 11, pp. 2083–2096, 2001.
C.A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, issue 6, pp. 532–537, 1993.
P. F. Felzenszwalb, D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59, no. 2, pp. 167–181, 2004.
X. Xu, S. Xu, L. Jin, E. Song, “Characteristic analysis of Otsu threshold and its applications,” Pattern Recognition Letters, vol. 32, no. 7, pp. 956–961, 2011.
R. Medina-Carnicer, F. Madrid-Cuevas, “Unimodal thresholding for edge detection,” Pattern Recognition, vol. 41, no. 7, pp. 2337–2346, 2008.
P.S. Liao, T.S. Chen, P.C. Chung, “A fast algorithm for multilevel thresholding,” Journal of Imformation Science and Engineering, vol. 17, pp. 713–727, 2001.
Y. Huang, S. Wang, “Multilevel thresholding methods for image segmentation with Otsu based on QPSO,” Proceedings of the Congress on Image and Signal Processing, 2008, pp. 701–705.
S. Arora, J. Acharya, A. Verma, P.K. Panigrahi, “Multilevel thresholding for image segmentation through a fast statistical recursive algorithm,” Pattern Recognition Letters, vol. 29, no. 2, pp. 119–125, 2008.
S.J. Mousavirad, H. Ebrahimpour-Komleh, “Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms,” Evolutionary Intelligence, vol. 10, no. 1–2, pp. 45–75, 2017.
A.Z. Arifin, A. Asano, “Image segmentation by histogram thresholding using hierarchical cluster analysis,” Pattern Recognition Letters, vol. 27, no. 13, pp. 1515–1521, 2006.
A.K.M. Khairuzzaman, S. Chaudhury, “Multilevel thresholding using grey wolf optimizer for image segmentation,” Expert Systems with Applications, vol. 86, pp. 64–76, 2017.
R. Melnyk, Yu. Kalychak, R. Tushnytskyy, “Absolute and relative classification of cloud regions by satellite image clustering,” Proceedings of the IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), pp. 45–49, 2019.
U. Ramer, “An iterative procedure for the polygonal approximation of plane curves,” Computer Graphics and Image Processing, vol. 1, issue 3, pp. 244–256, 1972.
D. Douglas, T. Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” The Canadian Cartographer, vol. 10, issue 2, pp. 112–122, 1973.
R. Melnyk, Yu. Kalychak, “Image thresholding by extrema coordinates of cumulative histogram,” International Journal Sensing, Computing & Control IJSCC, vol. 4, issue 1, pp. 1–9, 2014.
Infrared satellite images of Europe, Clouds in Europe at night and in the evening. Weather Europe, Satellite Weather Europe, SAT24.com, [Online]. Available at: https://es.sat24.com/en.
Satellite images of Ukraine, [Online]. Available at: https://meteo.gov.ua/33345/satellite.
Precipitation in Finland, [Online]. Available at: https://en.ilmatieteenlaitos.fi/rain-and-cloudiness.
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