Yıl 2010,
Cilt: 25 Sayı: 2, 0 - , 15.02.2013
Öz
Görüntü bölütleme bir görüntünün bir ya da daha fazla karakteristiğe veya özelliğe göre sınıf ya da altkümedenilen bölgelerine ayrılması, aynı karakteristiğe sahip, ilgilenilen alanların arka plandan ve diğer alanlardanayrılarak belirgin hale getirilmesidir. Görüntü işlemede gerçekleştirilen en zor adım görüntü bölütlemedir. Dahasonra yapılacak olan görüntü analizlerinin ve ilgili uygulamaların başarılı olması büyük ölçüde görüntübölütlemenin başarısına bağlı olmaktadır. Bu çalışmada çeşitli görüntüler özörgütlemeli harita (SOM) ağları vegri düzey eş oluşum matrisleri (GLCM) kullanılarak bölütlenmiştir. Bu yöntemlerin görüntü bölütlemeuygulamalarındaki performansları değerlendirilmiştir. Sonuçta bu yöntemlerin görüntü bölütlenmeuygulamalarında %90 başarılı gösterdiği görülmüştür.
Anahtar Kelimeler
Görüntü bölütleme özörgütlemeli harita ağları gri düzey eş oluşum matrisleri.
Kaynakça
- Koh, J., Suk, M., Bhandarkar, S.M., “A multilayer
- Kohonen's self-organizing feature map for
- range image segmentation”, IEEE International
- Conference on Neural Networks, San
- Francisco, CA, USA, vol.3., 1270-1275, 1993.
- Egmont-Petersen, M., Ridder, D., Handels, H.,
- “Image Processing with Neural Networks-a Review”,
- Pattern Recognition, Vol.35, 2279-2301, 2002.
- Murino V., Vernazza G., “Artificial Neural
- Networks for Image Analysis and Computer
- Vision”, Image and Vision Computing,
- Vol.19, Num.9, 583-584, 2001.
- Kohonen, T., “The Self-Organizing Map”,
- Proceedings of Institute of Electrical and
- Electronics Engineers, Vol.78, 1464-1480, 1990.
- Güler, İ., Demirhan, A., Karakış, R.,
- “Interpretation of MR images using selforganizing
- maps and knowledge-based expert
- systems”, Digital Signal Processing, In Press,
- doi:10.1016/j.dsp.2008.08.002, 2009.
- Haralick, R.M., Shanmugam, K. ve Dinstein, I.,
- “Textural Features for Image Classification”,
- IEEE Transactions on Systems, Man and
- Cybernetics, Vol. SMC-3, No.6, 610-621, 1973.
- Beliakov, G., James, S. ve Troiano, L., “Texture
- recognition by using GLCM and various
- aggregation functions”, IEEE International
- Conference on Fuzzy Systems (FUZZ-IEEE
- , Hong Kong, 1472-1476, 2008.
- Baraldi, A., Parmiggiani, F., “An investigation of
- the textural characteristics associated withgray
- level cooccurrence matrix statistical parameters”,
- IEEE Transactions on Geoscience and Remote
- Sensing, 33(2), 293-304, 1995.
- Zaim, A., Sawalha, A., Quweider, M., Iglesias, J.,
- Tang, R., “A New Method for Iris Recognition
- using Gray-Level Coccurence Matrix”, IEEE
- International Conference on
- Electro/information Technology, Michigan,
- U.S.A, 350-353, 2006.
- Hu, Y., Zhao, C., Wang, H., “Directional
- Analysis of Texture Images Using Gray Level
- Co-Occurrence Matrix”, 2008 IEEE Pacific-
- Asia Workshop on Computational Intelligence
- and Industrial Application, Wuhan, China, Vol.
- , 277-281, 2008.
- Amornrit, P., Jouvencel, B., Tomas, S., “A new
- method of pipeline detection in sonar imagery
- using Self-Organizing Maps”, IEEE/RSJ
- International Conference on Intelligent Robots
- and Systems, (IROS 2003), Nevada, Vol.1, 541-
- , 2003.
- Siqueira, M.L., Gasperin, C.V., Scharcanski, J.,
- Zielinsky, P., Navaux, P.O.A.,
- “Echocardiographic image sequence
- segmentation using self-organizing maps”,
- Neural Networks for Signal Processing X,
- Proceedings of the 2000 IEEE Signal
- Processing Society Workshop, Sydney,
- Australia, Vol.2, 594-603, 2000.
- Zizzari, A., Seiffert, U., Michaelis, B.,
- Gademann, G., Swiderski, S., “Detection of
- tumor in digital images of the brain”,
- Proceedings of the International Conference
- on Signal Processing, Pattern Recognition and
- Applications, Rhodes, Greece, 132-137, 2001.
- Kohonen, T., “Self-Organizing Formation of
- Topologically Correct Feature Maps”, Biological
- Cybernetics, 43(1), 59-69, 1982.
- Haykin, S., Neural Networks: A
- Comprehensive Foundation, Prentice-Hall,
- New Jersey, 1999.
- Şengür, A., Türkoğlu, İ., ve İnce, M.C.,
- “Eğiticisiz Yapay Sinir Ağları ile Görüntü
- Bölütleme Uygulamaları”, IEEE 13. Sinyal
- İşleme ve İletişim Uygulamaları Kurultayı,
- (SIU 2005), 271-274, Kayseri, 2005.
- Kotropoulos, C., Pitas, I., “Self-Organizing Maps
- and Their Applications in Image Processing,
- Information Organization, and Retrieval”,
- Nonlinear Signal and Image Processing,
- Editör: Barner, K.E, Arce, G.E., CRC Press, 387-
- , 2004.
- Uriarte, E.A. ve Martín, F.D., “Topology
- Preservation in SOM”, Proceedings of World
- Academy Of Science, Engineering And
- Technology, Vol.15, 187-190, 2006.
- Gonzales, R.C., Woods, R.E., Digital Image
- Processing, Prentice Hall, New Jersey, 2002.
- İnternet: Helsinki University of Technology,
- Department of Computer Science and
- Engineering, “SOM Toolbox”,
- http://www.cis.hut.fi/projects/somtoolbox/, 2005.
- Vesanto, J., Himberg, J., Alhoniemi, E.,
- Parhankangas, J., “SOM Toolbox for Matlab 5”,
- Technical Report A57, Neural Networks
- Research Centre, Helsinki University of
- Technology, Helsinki, Finland, 2000.
Yıl 2010,
Cilt: 25 Sayı: 2, 0 - , 15.02.2013
Öz
Kaynakça
- Koh, J., Suk, M., Bhandarkar, S.M., “A multilayer
- Kohonen's self-organizing feature map for
- range image segmentation”, IEEE International
- Conference on Neural Networks, San
- Francisco, CA, USA, vol.3., 1270-1275, 1993.
- Egmont-Petersen, M., Ridder, D., Handels, H.,
- “Image Processing with Neural Networks-a Review”,
- Pattern Recognition, Vol.35, 2279-2301, 2002.
- Murino V., Vernazza G., “Artificial Neural
- Networks for Image Analysis and Computer
- Vision”, Image and Vision Computing,
- Vol.19, Num.9, 583-584, 2001.
- Kohonen, T., “The Self-Organizing Map”,
- Proceedings of Institute of Electrical and
- Electronics Engineers, Vol.78, 1464-1480, 1990.
- Güler, İ., Demirhan, A., Karakış, R.,
- “Interpretation of MR images using selforganizing
- maps and knowledge-based expert
- systems”, Digital Signal Processing, In Press,
- doi:10.1016/j.dsp.2008.08.002, 2009.
- Haralick, R.M., Shanmugam, K. ve Dinstein, I.,
- “Textural Features for Image Classification”,
- IEEE Transactions on Systems, Man and
- Cybernetics, Vol. SMC-3, No.6, 610-621, 1973.
- Beliakov, G., James, S. ve Troiano, L., “Texture
- recognition by using GLCM and various
- aggregation functions”, IEEE International
- Conference on Fuzzy Systems (FUZZ-IEEE
- , Hong Kong, 1472-1476, 2008.
- Baraldi, A., Parmiggiani, F., “An investigation of
- the textural characteristics associated withgray
- level cooccurrence matrix statistical parameters”,
- IEEE Transactions on Geoscience and Remote
- Sensing, 33(2), 293-304, 1995.
- Zaim, A., Sawalha, A., Quweider, M., Iglesias, J.,
- Tang, R., “A New Method for Iris Recognition
- using Gray-Level Coccurence Matrix”, IEEE
- International Conference on
- Electro/information Technology, Michigan,
- U.S.A, 350-353, 2006.
- Hu, Y., Zhao, C., Wang, H., “Directional
- Analysis of Texture Images Using Gray Level
- Co-Occurrence Matrix”, 2008 IEEE Pacific-
- Asia Workshop on Computational Intelligence
- and Industrial Application, Wuhan, China, Vol.
- , 277-281, 2008.
- Amornrit, P., Jouvencel, B., Tomas, S., “A new
- method of pipeline detection in sonar imagery
- using Self-Organizing Maps”, IEEE/RSJ
- International Conference on Intelligent Robots
- and Systems, (IROS 2003), Nevada, Vol.1, 541-
- , 2003.
- Siqueira, M.L., Gasperin, C.V., Scharcanski, J.,
- Zielinsky, P., Navaux, P.O.A.,
- “Echocardiographic image sequence
- segmentation using self-organizing maps”,
- Neural Networks for Signal Processing X,
- Proceedings of the 2000 IEEE Signal
- Processing Society Workshop, Sydney,
- Australia, Vol.2, 594-603, 2000.
- Zizzari, A., Seiffert, U., Michaelis, B.,
- Gademann, G., Swiderski, S., “Detection of
- tumor in digital images of the brain”,
- Proceedings of the International Conference
- on Signal Processing, Pattern Recognition and
- Applications, Rhodes, Greece, 132-137, 2001.
- Kohonen, T., “Self-Organizing Formation of
- Topologically Correct Feature Maps”, Biological
- Cybernetics, 43(1), 59-69, 1982.
- Haykin, S., Neural Networks: A
- Comprehensive Foundation, Prentice-Hall,
- New Jersey, 1999.
- Şengür, A., Türkoğlu, İ., ve İnce, M.C.,
- “Eğiticisiz Yapay Sinir Ağları ile Görüntü
- Bölütleme Uygulamaları”, IEEE 13. Sinyal
- İşleme ve İletişim Uygulamaları Kurultayı,
- (SIU 2005), 271-274, Kayseri, 2005.
- Kotropoulos, C., Pitas, I., “Self-Organizing Maps
- and Their Applications in Image Processing,
- Information Organization, and Retrieval”,
- Nonlinear Signal and Image Processing,
- Editör: Barner, K.E, Arce, G.E., CRC Press, 387-
- , 2004.
- Uriarte, E.A. ve Martín, F.D., “Topology
- Preservation in SOM”, Proceedings of World
- Academy Of Science, Engineering And
- Technology, Vol.15, 187-190, 2006.
- Gonzales, R.C., Woods, R.E., Digital Image
- Processing, Prentice Hall, New Jersey, 2002.
- İnternet: Helsinki University of Technology,
- Department of Computer Science and
- Engineering, “SOM Toolbox”,
- http://www.cis.hut.fi/projects/somtoolbox/, 2005.
- Vesanto, J., Himberg, J., Alhoniemi, E.,
- Parhankangas, J., “SOM Toolbox for Matlab 5”,
- Technical Report A57, Neural Networks
- Research Centre, Helsinki University of
- Technology, Helsinki, Finland, 2000.
Toplam 98 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 15 Şubat 2013 |
| Gönderilme Tarihi | 15 Şubat 2013 |
| Yayımlandığı Sayı | Yıl 2010 Cilt: 25 Sayı: 2 |