Ekualisasi Histogram Dan Algoritma Kultural Untuk Segmentasi Citra Pantai
Keywords:coastline, histogram equalization, image quantization, cultural algorithm
This paper proposes a coastal image segmentation framework to cluster land and ocean pixel intensities to obtain coastline. The segmentation method commonly used is segmentation with the Otsu and Kapur methods, the problem with this method is that it can only segment images with one threshold value, and the complexity increases as the complexity of the image being processed increases. So that it produces output with less than optimal results and requires a long processing time. Therefore, an optimization method is proposed using histogram equalization which aims to increase the contrast value of the test image so that it is easier to segment, then proceed with image clustering using a cultural algorithm with multiple threshold values, so that the intensity of pixels representing land and water areas can be clustered with good. The target of the application of these two methods is to optimize in order to improve the performance of the test image segmentation. In addition, the advantage of this clustering method is that it produces a compressed output image so that it will be more efficient in terms of further processing, storage, and data transmission. The measurement methods applied are the measurement of processing time, Feature Similarity Index Measure (FSIM), and Structural Similarity Index Measure (SSIM) to compare the similarity of features and structures between the test image and the resulting output image. Where the proposed method has been able to cluster classes based on the configured threshold value, so as to be able to segment land and water areas.
C. A. B. Mello, T. J. dos Santos, H. R. Medeiros, and P. S. Pereira, “Shoreline Segmentation as a Proxy to Coastal Erosion Detection,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics, Oct. 2013, pp. 1217–1222. doi: 10.1109/SMC.2013.211.
S. Al Mansoori and F. Al-Marzouqi, “Coastline Extraction using Satellite Imagery and Image Processing Techniques,” International Journal of Current Engineering and Technology, vol. 6, Aug. 2016.
M. Lipakis and N. Chrysoulakis, “BEACHMED-e / OpTIMAL Beach Erosion Monitoring 81 Shoreline extraction using satellite imagery,” 2008. https://www.semanticscholar.org/paper/BEACHMED-e-%2F-OpTIMAL-Beach-Erosion-Monitoring-81-Lipakis-Chrysoulakis/41f4e334c044d80ed05c7fdad431ce9bbe58c75e (accessed Sep. 09, 2022).
“A new video monitoring system in support of Coastal Zone Management at Apulia Region, Italy - ScienceDirect.” https://www.sciencedirect.com/science/article/abs/pii/S0964569117303253 (accessed Sep. 09, 2022).
I. M. O. Widyantara, I. M. D. A. Putra, and I. B. P. Adnyana, “COVIMOS: A Coastal Video Monitoring System,” Journal of Electrical, Electronics and Informatics, vol. 1, no. 1, Art. no. 1, Feb. 2017, doi: 10.24843/JEEI.2017.v01.i01.p01.
“A comparative analysis of entropy based segmentation with Otsu method for gray and color images | IEEE Conference Publication | IEEE Xplore.” https://ieeexplore.ieee.org/document/8203655 (accessed Sep. 09, 2022).
“A new method for gray-level picture thresholding using the entropy of the histogram - ScienceDirect.” https://www.sciencedirect.com/science/article/abs/pii/0734189X85901252 (accessed Sep. 09, 2022).
I. Irwanto, Y. Purwananto, and R. Soelaiman, “Optimasi Kinerja Algoritma Klasterisasi K-Means untuk Kuantisasi Warna Citra,” JTITS, vol. 1, no. 1, pp. A197–A202, Sep. 2012, doi: 10.12962/j23373539.v1i1.631.
Z. Huang, Z. Wang, J. Zhang, Q. Li, and Y. Shi, “Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization,” Optik, vol. 226, p. 165877, Jan. 2021, doi: 10.1016/j.ijleo.2020.165877.
“Evolution Strategy Histogram Equalization - File Exchange - MATLAB Central.” https://www.mathworks.com/matlabcentral/fileexchange/105370-evolution-strategy-histogram-equalization (accessed Sep. 09, 2022).
“Cultural Algorithm Image Quantization - File Exchange - MATLAB Central.” https://www.mathworks.com/matlabcentral/fileexchange/105280-cultural-algorithm-image-quantization (accessed Sep. 09, 2022).
Z. Mao and M. Liu, “An improved multiobjective cultural algorithm with a multistrategy knowledge base,” Appl Intell, vol. 52, no. 2, pp. 1157–1187, Jan. 2022, doi: 10.1007/s10489-021-02313-6.
“Analisa Teknik Adaptive Histogram Equalization dan Contrast Stretching untuk Perbaikan Kualitas Citra - Neliti.” https://www.neliti.com/publications/247346/analisa-teknik-adaptive-histogram-equalization-dan-contrast-stretching-untuk-per (accessed Sep. 09, 2022).
R. C. Gonzalez, R. E. Woods, and B. R. Masters, “Digital Image Processing, Third Edition,” J. Biomed. Opt., vol. 14, no. 2, p. 029901, 2009, doi: 10.1117/1.3115362.
S. Mousavi, “Bio-Inspired Fossil Image Segmentation for Paleontology,” International Journal of Computational Engineering Science, vol. 12, pp. 5243–5249, Jul. 2022.
R. Munir, “Aplikasi Image Thresholding untuk Segmentasi Objek,” SNATI, 2006, Accessed: Sep. 09, 2022. [Online]. Available: https://journal.uii.ac.id/Snati/article/view/1521
L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386, Aug. 2011, doi: 10.1109/TIP.2011.2109730.
R. Dosselmann and X. D. Yang, “A Formal Assessment of the Structural Similarity Index,” p. 15, 2008.
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