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BENZETİMLİ TAVLAMA TEKNİĞİNİ KULLANAN ENİYİLENMİŞ GÖRÜNTÜ VE VİDEO İŞLEME ÜZERİNE KISA BİR LİTERATÜR ARAŞTIRMASI

Yıl 2018, Cilt: 8 Sayı: 2, 35 - 40, 30.11.2018

Öz



Günümüzde benzetimli
tavlama (simulated annealing) tekniği popüler bir teknik olarak eniyileme için
yapay zekâ alanında sıklıkla kullanılmaktadır. Bu tekniğin; görüntü işleme,
video işleme, yazılım ve diğer alanlarda ele alınan problemlerin daha kısa
sürede en uygun sonuçla (optimal) çözümünde gösterdiği önemli başarı nedeniyle
geçtiğimiz yıllar içerisinde literatürdeki çalışmalarda tercih edilme ve
kullanım oranı artmıştır. Çalışmamızda 2010 ilâ 2018 yılları arasında
yayınlanmış görüntü ve video işleme problemlerinin ele alındığı 10 adet yayın
incelenerek literatürde gelinen en son durum sistematik bir biçimde ortaya
konularak bulgular üzerinden yorumlanmıştır. Buna göre benzetimli tavlama
tekniği ve bununla melezleme yoluyla oluşturulan yaklaşımlar kombinasyonel
eniyileme problemlerinin daha uygun sonuçlarla daha kısa sürede ve yüksek başarımlı
olarak çözülebilmesine olanak sunmaktadır.

Kaynakça

  • Cerny, V. (1985). A Thermodinamical Approach to the Traveling Salesman Problem: an Efficient Simulated Annealing Algorithm. J. Optimiz. Theory Appl., 4, 41-55. Chang, Y.-L. (2011). A simulated annealing feature extraction approach for hyperspectral images. Future Generation Computer Systems, 27(4), 419-426, ISSN 0167-739X, doi: 10.1016/j.future.2010.08.008. Everts, M.H., Bekker, H., Jalba, A.C., & Roerdink, J.B.T.M. (2007). Particle based image segmentation with simulated annealing. SIREN: Scientific ICT Research Event Netherlands, 30 October 2007, TU Delft (poster). Fang, L., Zuo, H., Pang, L., Yang, Z., Zhang, X., & Zhu, J. (2018) Image reconstruction through thin scattering media by simulated annealing algorithm. Optics and Lasers in Engineering, 106, 105-110, ISSN 0143-8166, doi: 10.1016/j.optlaseng.2018.02.020. Fung, Y-H., & Chan, Y-H. (2006). A simulated annealing restoration algorithm for restoring halftoned color-quantized images. Signal Processing: Image Communication, 21(4), 280-292. Karasulu, B. (2010). Videolarda hareketli nesne tespiti ve takibi için benzetimli tavlama tabanlı bir başarım eniyileme yaklaşımı. Doktora tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü, 255 Sayfa, İzmir, Türkiye. Kaya, M. (2005). Image Clustering and Compression Using An Annealed Fuzzy Hopfield Neural Network. International Journal of Signal Processing, World Academy of Science, Engineering and Technology, 1-2, 80-88. Kirkpatrick, S., Gelatt, C., & Vecchi, M., 1983, Optimization by Simulated Annealing, Science, 220:671-680. Li, X., & Ma, L. (2012). Minimizing binary functions with simulated annealing algorithm with applications to binary tomography. Computer Physics Communications, 183(2), 309-315, ISSN 0010-4655, doi:10.1016/j.cpc.2011.10.011. Lin, G. S., Chang, Y. T., & Lie, W. N. (2010). A Framework of Enhancing Image Steganography With Picture Quality Optimization and Anti-Steganalysis Based on Simulated Annealing Algorithm. IEEE Transactions on Multimedia, 12(5), 345-357, doi: 10.1109/TMM.2010.2051243. Liu, J., Tong, X., Li, W., Wang, T., Zhang, Y. & Wang, H. (2009). Automatic player detection, labeling and tracking in broadcast soccer video. Pattern Recognition Letters, Video-based Object and Event Analysis, 30(2), 103-113. Martins, T.d.C., Tsuzuki, M.d.S.G., Camargo, E.D.L.B.d., Lima, R.G., Moura, F.S.d., & Amato, M.B.P. (2016). Interval Simulated Annealing applied to Electrical Impedance Tomography image reconstruction with fast objective function evaluation. Computers & Mathematics with Applications, 72(5), 1230-1243, ISSN 0898-1221, doi:10.1016/j.camwa.2016.06.021. Medjahed, S.A., & Ouali, M. (2018). Band selection based on optimization approach for hyperspectral image classification. The Egyptian Journal of Remote Sensing and Space Science, In Press (Corrected Proof), ISSN 1110-9823, doi:10.1016/j.ejrs.2018.01.003. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. (1953). Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21(6), 1087-1092. Schwegmann, C. P., Kleynhans, W., & Salmon, B. P. (2014). Simulated annealing CFAR threshold selection for South African ship detection in ASAR imagery. IEEE Geoscience and Remote Sensing Symposium, 561-564. Quebec City, QC, 2014. doi: 10.1109/IGARSS.2014.6946484. Sharma, N. (2008). Simulation Optimization Using Simulated Annealing: A Network-based Implementation and Study of Cooling Schedules. VDM Verlag Dr. Müller, Saarbrücken, Germany, 116p., ISBN-10: 3639085957, ISBN-13: 978-3639085952. Singh, M. P., & Dixit, R. S. (2013). Optimization of stochastic networks using simulated annealing for the storage and recalling of compressed images using SOM. Engineering Applications of Artificial Intelligence, 26(10), 2383-2396, ISSN 0952-1976, doi:10.1016/j.engappai.2013.07.003. Tavares, R.S., Sato, A.K., Martins, T.C., Lima, R.G., & Tsuzuki, M.S.G. (2017). GPU acceleration of absolute EIT image reconstruction using simulated annealing. Biomedical Signal Processing and Control, In Press (Corrected Proof), ISSN 1746-8094, doi:10.1016/j.bspc.2017.02.007. Wang, P., Lin, J.S., & Wang, M. (2015). An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization. Journal of Applied Research and Technology, 13(2), 197-204, ISSN 1665-6423, doi:10.1016/j.jart.2015.06.018. Zhou, X., & Xu, C-Z. (2007). Efficient algorithms of video replication and placement on a cluster of streaming servers. Journal of Network and Computer Applications, 30(2), 515-540.

A BRIEF LITERATURE REVIEW ON OPTIMIZED IMAGE AND VIDEO PROCESSING USING SIMULATED ANNEALING TECHNIQUE

Yıl 2018, Cilt: 8 Sayı: 2, 35 - 40, 30.11.2018

Öz



Nowadays, the
simulated annealing is often used as a popular technique in the field of
artificial intelligence for optimization. This technique achieves a significant
success for solving the problems in the field of image processing, video
processing, software, and others that its solution is optimally obtained in a
shorter time. In the last years due to its success, its rate of preference has
increased. In our study, 10 publications dealing with image and video
processing problems published between 2010 and 2018 were examined, thus, the
latest state in the literature was introduced in a systematic way and
interpreted through findings. Accordingly, the combination of the simulated
annealing technique and hybrid approaches allow the optimization problems to be
solved in a shorter time and with higher efficiency with more appropriate
results.

Kaynakça

  • Cerny, V. (1985). A Thermodinamical Approach to the Traveling Salesman Problem: an Efficient Simulated Annealing Algorithm. J. Optimiz. Theory Appl., 4, 41-55. Chang, Y.-L. (2011). A simulated annealing feature extraction approach for hyperspectral images. Future Generation Computer Systems, 27(4), 419-426, ISSN 0167-739X, doi: 10.1016/j.future.2010.08.008. Everts, M.H., Bekker, H., Jalba, A.C., & Roerdink, J.B.T.M. (2007). Particle based image segmentation with simulated annealing. SIREN: Scientific ICT Research Event Netherlands, 30 October 2007, TU Delft (poster). Fang, L., Zuo, H., Pang, L., Yang, Z., Zhang, X., & Zhu, J. (2018) Image reconstruction through thin scattering media by simulated annealing algorithm. Optics and Lasers in Engineering, 106, 105-110, ISSN 0143-8166, doi: 10.1016/j.optlaseng.2018.02.020. Fung, Y-H., & Chan, Y-H. (2006). A simulated annealing restoration algorithm for restoring halftoned color-quantized images. Signal Processing: Image Communication, 21(4), 280-292. Karasulu, B. (2010). Videolarda hareketli nesne tespiti ve takibi için benzetimli tavlama tabanlı bir başarım eniyileme yaklaşımı. Doktora tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü, 255 Sayfa, İzmir, Türkiye. Kaya, M. (2005). Image Clustering and Compression Using An Annealed Fuzzy Hopfield Neural Network. International Journal of Signal Processing, World Academy of Science, Engineering and Technology, 1-2, 80-88. Kirkpatrick, S., Gelatt, C., & Vecchi, M., 1983, Optimization by Simulated Annealing, Science, 220:671-680. Li, X., & Ma, L. (2012). Minimizing binary functions with simulated annealing algorithm with applications to binary tomography. Computer Physics Communications, 183(2), 309-315, ISSN 0010-4655, doi:10.1016/j.cpc.2011.10.011. Lin, G. S., Chang, Y. T., & Lie, W. N. (2010). A Framework of Enhancing Image Steganography With Picture Quality Optimization and Anti-Steganalysis Based on Simulated Annealing Algorithm. IEEE Transactions on Multimedia, 12(5), 345-357, doi: 10.1109/TMM.2010.2051243. Liu, J., Tong, X., Li, W., Wang, T., Zhang, Y. & Wang, H. (2009). Automatic player detection, labeling and tracking in broadcast soccer video. Pattern Recognition Letters, Video-based Object and Event Analysis, 30(2), 103-113. Martins, T.d.C., Tsuzuki, M.d.S.G., Camargo, E.D.L.B.d., Lima, R.G., Moura, F.S.d., & Amato, M.B.P. (2016). Interval Simulated Annealing applied to Electrical Impedance Tomography image reconstruction with fast objective function evaluation. Computers & Mathematics with Applications, 72(5), 1230-1243, ISSN 0898-1221, doi:10.1016/j.camwa.2016.06.021. Medjahed, S.A., & Ouali, M. (2018). Band selection based on optimization approach for hyperspectral image classification. The Egyptian Journal of Remote Sensing and Space Science, In Press (Corrected Proof), ISSN 1110-9823, doi:10.1016/j.ejrs.2018.01.003. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. (1953). Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21(6), 1087-1092. Schwegmann, C. P., Kleynhans, W., & Salmon, B. P. (2014). Simulated annealing CFAR threshold selection for South African ship detection in ASAR imagery. IEEE Geoscience and Remote Sensing Symposium, 561-564. Quebec City, QC, 2014. doi: 10.1109/IGARSS.2014.6946484. Sharma, N. (2008). Simulation Optimization Using Simulated Annealing: A Network-based Implementation and Study of Cooling Schedules. VDM Verlag Dr. Müller, Saarbrücken, Germany, 116p., ISBN-10: 3639085957, ISBN-13: 978-3639085952. Singh, M. P., & Dixit, R. S. (2013). Optimization of stochastic networks using simulated annealing for the storage and recalling of compressed images using SOM. Engineering Applications of Artificial Intelligence, 26(10), 2383-2396, ISSN 0952-1976, doi:10.1016/j.engappai.2013.07.003. Tavares, R.S., Sato, A.K., Martins, T.C., Lima, R.G., & Tsuzuki, M.S.G. (2017). GPU acceleration of absolute EIT image reconstruction using simulated annealing. Biomedical Signal Processing and Control, In Press (Corrected Proof), ISSN 1746-8094, doi:10.1016/j.bspc.2017.02.007. Wang, P., Lin, J.S., & Wang, M. (2015). An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization. Journal of Applied Research and Technology, 13(2), 197-204, ISSN 1665-6423, doi:10.1016/j.jart.2015.06.018. Zhou, X., & Xu, C-Z. (2007). Efficient algorithms of video replication and placement on a cluster of streaming servers. Journal of Network and Computer Applications, 30(2), 515-540.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Bahadır Karasulu

Yayımlanma Tarihi 30 Kasım 2018
Gönderilme Tarihi 4 Temmuz 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 2

Kaynak Göster

APA Karasulu, B. (2018). BENZETİMLİ TAVLAMA TEKNİĞİNİ KULLANAN ENİYİLENMİŞ GÖRÜNTÜ VE VİDEO İŞLEME ÜZERİNE KISA BİR LİTERATÜR ARAŞTIRMASI. Ejovoc (Electronic Journal of Vocational Colleges), 8(2), 35-40.