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EŞİKLEME METOTLARININ SEGMENTASYON ÜZERİNDEKİ ETKİLERİNİN GÜVE ALEV OPTİMİZASYONU ALGORİTMASI KULLANILARAK KARŞILAŞTIRILMASI

Yıl 2023, Cilt: 26 Sayı: 2, 517 - 531, 03.06.2023

Murat Karakoyun

https://doi.org/10.17780/ksujes.1222041
Cited By: 1

Öz

Segmentasyon görüntü işleme uygulamalarında başarıyı doğrudan etkileyen önemli bir ön işlem adımıdır. Segmentasyon süreci için kullanılan birçok yöntem ve yaklaşım mevcuttur. Eşikleme bu yöntemler içerisinde sıklıkla kullanılan bir yaklaşımdır. Eşikleme için önerilen birçok yaklaşım bulunmaktadır. Bu çalışmada moth flame algoritması kullanılarak altı farklı eşikleme yaklaşımı uygunluk fonksiyonu olarak kullanılmış ve bu yaklaşımlardan elde edilen sonuçlar karşılaştırılmıştır. Deneysel çalışmalarda 10 farklı görüntünün yedi farklı eşik seviyesi üzerinde çalışılmıştır. Üç farklı metrik ile yapılan kıyaslamalarda Otsu metodunun genel olarak daha başarılı olduğu görülmüştür. Ayrıca minimum cross entropy ve Renyi entropilerinin de alternatif olarak kullanılabileceği gözlemlenmiştir.

Anahtar Kelimeler

Kapur güve alev optimizasyonu Otsu segmentasyon eşikleme

Kaynakça

  • Abdel-Basset, M., Mohamed, R., AbdelAziz, N. M., & Abouhawwash, M. (2022). HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Systems with Applications, 190, 116145. https://doi.org/10.1016/j.eswa.2021.116145
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2015a). Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications, 42(3), 1573-1601. https://doi.org/10.1016/j.eswa.2014.09.049
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2015b). Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications, 42(22), 8707-8730. https://doi.org/10.1016/j.eswa.2015.07.025
  • Brooks, A. C., Zhao, X., & Pappas, T. N. (2008). Structural similarity quality metrics in a coding context: exploring the space of realistic distortions. IEEE Transactions on image processing, 17(8), 1261-1273. https://doi.org/10.1109/TIP.2008.926161 Cai, Y., Mi, S., Yan, J., Peng, H., Luo, X., Yang, Q., & Wang, J. (2022). An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Information Sciences, 587, 473-484. https://doi.org/10.1016/j.ins.2021.12.058
  • Chen, Y., Wang, M., Heidari, A. A., Shi, B., Hu, Z., Zhang, Q., Chen, H., Mafarja, M., & Turabieh, H. (2022). Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Systems with Applications, 194, 116511. https://doi.org/10.1016/j.eswa.2022.116511 https://doi.org/10.1016/j.eswa.2022.116511
  • De Albuquerque, M. P., Esquef, I. A., Mello, A. R. G., & De Albuquerque, M. P. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059-1065. https://doi.org/10.1016/j.patrec.2004.03.003
  • Günay, M., & Taze, M. (2022). Mikroskobik Görüntülerde Multipl Miyelom Plazma Hücrelerinin Tespiti. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(2), 145-154. https://doi.org/10.17780/ksujes.1120829
  • Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Paper presented at the 2010 20th international conference on pattern recognition.https://doi.org/ 10.1109/ICPR.2010.579
  • Houssein, E. H., Helmy, B. E., Oliva, D., Jangir, P., Premkumar, M., Elngar, A. A., & Shaban, H. (2022). An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer. Biomedical Signal Processing and Control, 73, 103401. https://doi.org/10.1016/j.bspc.2021.103401
  • Huang, C., Li, X., & Wen, Y. (2021). AN OTSU image segmentation based on fruitfly optimization algorithm. Alexandria Engineering Journal, 60(1), 183-188. https://doi.org/10.1016/j.aej.2020.06.054
  • Ishak, A. B. (2017). A two-dimensional multilevel thresholding method for image segmentation. Applied Soft Computing, 52, 306-322. https://doi.org/10.1016/j.asoc.2016.10.034
  • Kalyani, R., Sathya, P., & Sakthivel, V. (2020). Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy. Engineering Science and Technology, an International Journal, 23(6), 1327-1341. https://doi.org/10.1016/j.jestch.2020.07.007
  • Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285. https://doi.org/10.1016/0734-189X(85)90125-2
  • Karakoyun, M., Baykan, N. A., & Hacibeyoglu, M. (2017a). Multi-level thresholding for image segmentation with swarm optimization algorithms. International Research Journal of Electronics & Computer Engineering, 3(3), 1. https://doi.org/10.24178/irjece.2017.3.3.01
  • Karakoyun, M., Gülcü, Ş., & Kodaz, H. (2021). D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Engineering Science and Technology, an International Journal, 24(6), 1455-1466. https://doi.org/10.1016/j.jestch.2021.03.011
  • Karakoyun, M., & Özkış, A. (2021). Transfer Fonksiyonları Kullanarak İkili Güve-Alev Optimizasyonu Algoritmalarının Geliştirilmesi ve Performanslarının Karşılaştırılması. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 3(2), 1-10.
  • Karakoyun, M., Saglam, A., Baykan, N. A., & Altun, A. A. (2017b). Non-locally color image segmentation for remote sensing images in different color spaces by using data-clustering methods. image, 10, 11.
  • Koc, I., Baykan, O. K., & Babaoglu, I. (2018). Multilevel image thresholding selection based on grey wolf optimizer. JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 21(4), 841-847. https://doi.org/10.2339/politeknik.389613
  • Li, Y., Cao, G., Wang, T., Cui, Q., & Wang, B. (2020). A novel local region-based active contour model for image segmentation using Bayes theorem. Information Sciences, 506, 443-456. https://doi.org/10.1016/j.ins.2019.08.021
  • Li, Y., Zhu, X., & Liu, J. (2020). An improved moth-flame optimization algorithm for engineering problems. Symmetry, 12(8), 1234. https://doi.org/10.3390/sym12081234
  • Ma, G., & Yue, X. (2022). An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Engineering Applications of Artificial Intelligence, 113, 104960. https://doi.org/10.1016/j.engappai.2022.104960
  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Naidu, M., Kumar, P. R., & Chiranjeevi, K. (2018). Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Engineering Journal, 57(3), 1643-1655. https://doi.org/10.1016/j.aej.2017.05.024
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Pal, N. R. (1996). On minimum cross-entropy thresholding. Pattern recognition, 29(4), 575-580. https://doi.org/10.1016/0031-3203(95)00111-5
  • Priyadharsini, R., & Sharmila, T. S. (2019). Object detection in underwater acoustic images using edge based segmentation method. Procedia Computer Science, 165, 759-765. https://doi.org/10.1016/j.procs.2020.01.015
  • Raj, A., Gautam, G., Abdullah, S. N. H. S., Zaini, A. S., & Mukhopadhyay, S. (2019). Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy. Image and Vision Computing, 91, 103792. https://doi.org/10.1016/j.imavis.2019.07.004
  • Ryalat, M. H., Dorgham, O., Tedmori, S., Al-Rahamneh, Z., Al-Najdawi, N., & Mirjalili, S. (2022). Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Computing and Applications, 1-19. https://doi.org/10.1007/s00521-022-08078-4
  • Sahoo, P., Wilkins, C., & Yeager, J. (1997). Threshold selection using Renyi's entropy. Pattern recognition, 30(1), 71-84. https://doi.org/10.1016/S0031-3203(96)00065-9
  • Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8-18.https://doi.org/10.4236/jcc.2019.73002
  • Satapathy, S. C., Sri Madhava Raja, N., Rajinikanth, V., Ashour, A. S., & Dey, N. (2018). Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Computing and Applications, 29(12), 1285-1307. https://doi.org/10.1007/s00521-016-2645-5
  • Selçuk, T., Bilal, N., Sarıca, S., Akben, B., & Alkan, A. (2017). Ses Tellerinde Oluşan Nodüllere Ait Şekilsel Özelliklerin Görüntü İşleme Teknikleriyle Otomatik Olarak Belirlenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20(4), 54-59. https://doi.org/10.17780/ksujes.349448
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  • Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. M. (2020). Moth–flame optimization algorithm: variants and applications. Neural Computing and Applications, 32(14), 9859-9884. https://doi.org/10.1007/s00521-019-04570-6
  • Tuba, E., Alihodzic, A., & Tuba, M. (2017). Multilevel image thresholding using elephant herding optimization algorithm. Paper presented at the 2017 14th international conference on engineering of modern electric systems (EMES). https://doi.org/10.1109/EMES.2017.7980424
  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on image processing, 13(4), 600-612. https://doi.org/10.1109/TIP.2003.819861
  • Xing, Z., & He, Y. (2021). Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Applied Soft Computing, 113, 107905. https://doi.org/10.1016/j.asoc.2021.107905
  • Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on image processing, 20(8), 2378-2386.https://doi.org/10.1109/TIP.2011.2109730
  • Zhao, S., Wang, P., Heidari, A. A., Chen, H., Turabieh, H., Mafarja, M., & Li, C. (2021). Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease. Computers in Biology and Medicine, 134, 104427. https://doi.org/10.1016/j.compbiomed.2021.104427

THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM

Yıl 2023, Cilt: 26 Sayı: 2, 517 - 531, 03.06.2023

Murat Karakoyun

https://doi.org/10.17780/ksujes.1222041
Cited By: 1

Öz

Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for the segmentation process. Thresholding is a frequently used approach among these methods. There are several suggested approaches to thresholding. In this study, six different thresholding approaches were used as the fitness functions using the moth flame algorithm and the results obtained from these approaches were compared. In experimental studies, seven different threshold levels of 10 different images were studied. In comparisons made with three different metrics, it was seen that the Otsu method was generally more successful. It has also been observed that the minimum cross entropy and Renyi entropies can be used as alternatives.

Anahtar Kelimeler

Kapur moth flame optimization Otsu segmentation thresholding

Kaynakça

  • Abdel-Basset, M., Mohamed, R., AbdelAziz, N. M., & Abouhawwash, M. (2022). HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Systems with Applications, 190, 116145. https://doi.org/10.1016/j.eswa.2021.116145
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2015a). Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications, 42(3), 1573-1601. https://doi.org/10.1016/j.eswa.2014.09.049
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2015b). Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications, 42(22), 8707-8730. https://doi.org/10.1016/j.eswa.2015.07.025
  • Brooks, A. C., Zhao, X., & Pappas, T. N. (2008). Structural similarity quality metrics in a coding context: exploring the space of realistic distortions. IEEE Transactions on image processing, 17(8), 1261-1273. https://doi.org/10.1109/TIP.2008.926161 Cai, Y., Mi, S., Yan, J., Peng, H., Luo, X., Yang, Q., & Wang, J. (2022). An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Information Sciences, 587, 473-484. https://doi.org/10.1016/j.ins.2021.12.058
  • Chen, Y., Wang, M., Heidari, A. A., Shi, B., Hu, Z., Zhang, Q., Chen, H., Mafarja, M., & Turabieh, H. (2022). Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Systems with Applications, 194, 116511. https://doi.org/10.1016/j.eswa.2022.116511 https://doi.org/10.1016/j.eswa.2022.116511
  • De Albuquerque, M. P., Esquef, I. A., Mello, A. R. G., & De Albuquerque, M. P. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059-1065. https://doi.org/10.1016/j.patrec.2004.03.003
  • Günay, M., & Taze, M. (2022). Mikroskobik Görüntülerde Multipl Miyelom Plazma Hücrelerinin Tespiti. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(2), 145-154. https://doi.org/10.17780/ksujes.1120829
  • Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Paper presented at the 2010 20th international conference on pattern recognition.https://doi.org/ 10.1109/ICPR.2010.579
  • Houssein, E. H., Helmy, B. E., Oliva, D., Jangir, P., Premkumar, M., Elngar, A. A., & Shaban, H. (2022). An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer. Biomedical Signal Processing and Control, 73, 103401. https://doi.org/10.1016/j.bspc.2021.103401
  • Huang, C., Li, X., & Wen, Y. (2021). AN OTSU image segmentation based on fruitfly optimization algorithm. Alexandria Engineering Journal, 60(1), 183-188. https://doi.org/10.1016/j.aej.2020.06.054
  • Ishak, A. B. (2017). A two-dimensional multilevel thresholding method for image segmentation. Applied Soft Computing, 52, 306-322. https://doi.org/10.1016/j.asoc.2016.10.034
  • Kalyani, R., Sathya, P., & Sakthivel, V. (2020). Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy. Engineering Science and Technology, an International Journal, 23(6), 1327-1341. https://doi.org/10.1016/j.jestch.2020.07.007
  • Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285. https://doi.org/10.1016/0734-189X(85)90125-2
  • Karakoyun, M., Baykan, N. A., & Hacibeyoglu, M. (2017a). Multi-level thresholding for image segmentation with swarm optimization algorithms. International Research Journal of Electronics & Computer Engineering, 3(3), 1. https://doi.org/10.24178/irjece.2017.3.3.01
  • Karakoyun, M., Gülcü, Ş., & Kodaz, H. (2021). D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Engineering Science and Technology, an International Journal, 24(6), 1455-1466. https://doi.org/10.1016/j.jestch.2021.03.011
  • Karakoyun, M., & Özkış, A. (2021). Transfer Fonksiyonları Kullanarak İkili Güve-Alev Optimizasyonu Algoritmalarının Geliştirilmesi ve Performanslarının Karşılaştırılması. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 3(2), 1-10.
  • Karakoyun, M., Saglam, A., Baykan, N. A., & Altun, A. A. (2017b). Non-locally color image segmentation for remote sensing images in different color spaces by using data-clustering methods. image, 10, 11.
  • Koc, I., Baykan, O. K., & Babaoglu, I. (2018). Multilevel image thresholding selection based on grey wolf optimizer. JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 21(4), 841-847. https://doi.org/10.2339/politeknik.389613
  • Li, Y., Cao, G., Wang, T., Cui, Q., & Wang, B. (2020). A novel local region-based active contour model for image segmentation using Bayes theorem. Information Sciences, 506, 443-456. https://doi.org/10.1016/j.ins.2019.08.021
  • Li, Y., Zhu, X., & Liu, J. (2020). An improved moth-flame optimization algorithm for engineering problems. Symmetry, 12(8), 1234. https://doi.org/10.3390/sym12081234
  • Ma, G., & Yue, X. (2022). An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Engineering Applications of Artificial Intelligence, 113, 104960. https://doi.org/10.1016/j.engappai.2022.104960
  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Naidu, M., Kumar, P. R., & Chiranjeevi, K. (2018). Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Engineering Journal, 57(3), 1643-1655. https://doi.org/10.1016/j.aej.2017.05.024
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Pal, N. R. (1996). On minimum cross-entropy thresholding. Pattern recognition, 29(4), 575-580. https://doi.org/10.1016/0031-3203(95)00111-5
  • Priyadharsini, R., & Sharmila, T. S. (2019). Object detection in underwater acoustic images using edge based segmentation method. Procedia Computer Science, 165, 759-765. https://doi.org/10.1016/j.procs.2020.01.015
  • Raj, A., Gautam, G., Abdullah, S. N. H. S., Zaini, A. S., & Mukhopadhyay, S. (2019). Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy. Image and Vision Computing, 91, 103792. https://doi.org/10.1016/j.imavis.2019.07.004
  • Ryalat, M. H., Dorgham, O., Tedmori, S., Al-Rahamneh, Z., Al-Najdawi, N., & Mirjalili, S. (2022). Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Computing and Applications, 1-19. https://doi.org/10.1007/s00521-022-08078-4
  • Sahoo, P., Wilkins, C., & Yeager, J. (1997). Threshold selection using Renyi's entropy. Pattern recognition, 30(1), 71-84. https://doi.org/10.1016/S0031-3203(96)00065-9
  • Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8-18.https://doi.org/10.4236/jcc.2019.73002
  • Satapathy, S. C., Sri Madhava Raja, N., Rajinikanth, V., Ashour, A. S., & Dey, N. (2018). Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Computing and Applications, 29(12), 1285-1307. https://doi.org/10.1007/s00521-016-2645-5
  • Selçuk, T., Bilal, N., Sarıca, S., Akben, B., & Alkan, A. (2017). Ses Tellerinde Oluşan Nodüllere Ait Şekilsel Özelliklerin Görüntü İşleme Teknikleriyle Otomatik Olarak Belirlenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20(4), 54-59. https://doi.org/10.17780/ksujes.349448
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  • Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. M. (2020). Moth–flame optimization algorithm: variants and applications. Neural Computing and Applications, 32(14), 9859-9884. https://doi.org/10.1007/s00521-019-04570-6
  • Tuba, E., Alihodzic, A., & Tuba, M. (2017). Multilevel image thresholding using elephant herding optimization algorithm. Paper presented at the 2017 14th international conference on engineering of modern electric systems (EMES). https://doi.org/10.1109/EMES.2017.7980424
  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on image processing, 13(4), 600-612. https://doi.org/10.1109/TIP.2003.819861
  • Xing, Z., & He, Y. (2021). Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Applied Soft Computing, 113, 107905. https://doi.org/10.1016/j.asoc.2021.107905
  • Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on image processing, 20(8), 2378-2386.https://doi.org/10.1109/TIP.2011.2109730
  • Zhao, S., Wang, P., Heidari, A. A., Chen, H., Turabieh, H., Mafarja, M., & Li, C. (2021). Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease. Computers in Biology and Medicine, 134, 104427. https://doi.org/10.1016/j.compbiomed.2021.104427
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Bilgisayar Mühendisliği
Yazarlar

Murat Karakoyun 0000-0002-0677-9313

Yayımlanma Tarihi 3 Haziran 2023
Gönderilme Tarihi 20 Aralık 2022
Yayımlandığı Sayı Yıl 2023Cilt: 26 Sayı: 2

Kaynak Göster

APA Karakoyun, M. (2023). THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 517-531. https://doi.org/10.17780/ksujes.1222041

Cited By

Elektron mikroskobundan elde edilen görüntülerin parçacık sayımında ön işleme tekniklerinin etkisi
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.54365/adyumbd.1348886
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