Research Article
Year 2021,
Volume: 2 Issue: 1, 1 - 6, 15.06.2021
Abstract
Görüntü Bölütleme, resim içinde birbirinden kolayca ayrılabilen ve kendi içinde homojen olabilen farklı bölgelerin elde edilmesinde kullanılan zor bir tekniktir. Bu makale yerden 5 ila 30 metre yükseklikte bir drone tarafından elde edilen 6000*4000 pixel boyutundaki kuşbakışı görüntülerinin kentsel görüntülerim anlamsal olarak bölütlemesini sunar. Bölütleme için Fuzzy C-Means (FCM) algoritması kullanılmıştır. Görüntüler üzerinde herhangi bir ön işleme yapmadan elde edilen bölütleme sonuçları Dice, Jaccard ve Mutual Information, benzerlik ölçütleri ile değerlendirilmiştir. Elde edilen sonuçlara göre FCM algoritması kentsel görüntüleri başarılı bir şekilde bölütleme yapmaktadır.
References
- 1. J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact WellSeparated Clusters", Journal of Cybernetics 3: 32-57, 1973.
- 2. J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New, 1981.
- 3, N. G. Seresht, R. Lourenzutti, A. R. Fayek, “A fuzzy clustering algorithm for developing predictive models in construction applications”, Applied Soft Computing, Vol.96, 2020,
- 4. F. Şişik, E. Sert, Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware”, Medical Hypotheses, Vol.136, 2020.
- 5. F. Di Martino, S. Sessa, “The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS”, Expert Systems with Applications, Vol.38(9), p:11829-11836, 2011.
- 6. J. Arora, M. Tushir, “An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation”, Procedia Computer Science, Vol.167, p:646-655, 2020.
- 7. C. Singh, A. Bala, “An unsupervised orthogonal rotation invariant moment based fuzzy C-means approach for the segmentation of brain magnetic resonance images”, Expert Systems with Applications, Vol.164, 2021.
- 8. F. Gamino-Sánchez, I. V. Hernández-Gutiérrez, A. J. Rosales-Silva, F. J. Gallegos-Funes, D. Mújica-Vargas, E. Ramos-Díaz, B. E. Carvajal-Gámez, J. M. V. Kinani, “Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise”, Engineering Applications of Artificial Intelligence, Vol.73, :31-49, 2018.
- 9. K, P. Ganesan, B.S. Sathish, J. M. M. Jenitha, K. B. Shaik, “Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space”, Procedia Computer Science, Vol.57, 2015.
- 10. Z. Tian, B. Li, “An Application of Fuzzy C-Means Based Clustering Technique in Smart Farming”, Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering, Vol.121, 2015.
- 11. A. M. Anter, A. E. Hassenian, D. Oliva, “An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural”, Expert Systems with Applications, Vol.118, p:340-354, 2019.
- 12. S. M. Zabihi, M-R Akbarzadeh-T, “Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets”, Vol.2012, Article ID 929085, 2012.
- 13. Semantic Drone Dataset: https://www.tugraz.at/index.php?id=22387
Year 2021,
Volume: 2 Issue: 1, 1 - 6, 15.06.2021
Abstract
References
- 1. J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact WellSeparated Clusters", Journal of Cybernetics 3: 32-57, 1973.
- 2. J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New, 1981.
- 3, N. G. Seresht, R. Lourenzutti, A. R. Fayek, “A fuzzy clustering algorithm for developing predictive models in construction applications”, Applied Soft Computing, Vol.96, 2020,
- 4. F. Şişik, E. Sert, Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware”, Medical Hypotheses, Vol.136, 2020.
- 5. F. Di Martino, S. Sessa, “The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS”, Expert Systems with Applications, Vol.38(9), p:11829-11836, 2011.
- 6. J. Arora, M. Tushir, “An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation”, Procedia Computer Science, Vol.167, p:646-655, 2020.
- 7. C. Singh, A. Bala, “An unsupervised orthogonal rotation invariant moment based fuzzy C-means approach for the segmentation of brain magnetic resonance images”, Expert Systems with Applications, Vol.164, 2021.
- 8. F. Gamino-Sánchez, I. V. Hernández-Gutiérrez, A. J. Rosales-Silva, F. J. Gallegos-Funes, D. Mújica-Vargas, E. Ramos-Díaz, B. E. Carvajal-Gámez, J. M. V. Kinani, “Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise”, Engineering Applications of Artificial Intelligence, Vol.73, :31-49, 2018.
- 9. K, P. Ganesan, B.S. Sathish, J. M. M. Jenitha, K. B. Shaik, “Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space”, Procedia Computer Science, Vol.57, 2015.
- 10. Z. Tian, B. Li, “An Application of Fuzzy C-Means Based Clustering Technique in Smart Farming”, Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering, Vol.121, 2015.
- 11. A. M. Anter, A. E. Hassenian, D. Oliva, “An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural”, Expert Systems with Applications, Vol.118, p:340-354, 2019.
- 12. S. M. Zabihi, M-R Akbarzadeh-T, “Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets”, Vol.2012, Article ID 929085, 2012.
- 13. Semantic Drone Dataset: https://www.tugraz.at/index.php?id=22387
There are 13 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 15, 2021 |
| Submission Date | November 26, 2020 |
| Acceptance Date | February 5, 2021 |
| Published in Issue | Year 2021 Volume: 2 Issue: 1 |
