CLOUDINESS IMAGES MULTILEVEL SEGMENTATION BY PIECEWISE LINEAR APPROXIMATION OF CUMULATIVE HISTOGRAM

Authors

  • Roman Melnyk
  • Ruslan Tushnytskyy
  • Roman Kvit

Keywords:

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.

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Published

2020-06-14

How to Cite

Melnyk, R., Tushnytskyy, R., & Kvit, R. (2020). CLOUDINESS IMAGES MULTILEVEL SEGMENTATION BY PIECEWISE LINEAR APPROXIMATION OF CUMULATIVE HISTOGRAM. International Journal of Computing, 19(2), 199-207. Retrieved from http://computingonline.net/computing/article/view/1762

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