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POİSSON MARKOV RASSAL ALANLARI İLE EĞİTİCİSİZ GÖRÜNTÜ BÖLÜTLEME

Year 2007, Volume: 2 Issue: 4, 305 - 321, 01.05.2007

Abdülkadir Sengur

Abstract

Son zamanlarda Markov Rassal Alanları temelli görüntü bölütleme yöntemleri bir hayli ilgi çekmiştir. MRF'ler genellikle Gauss dağılımlı şartlı modeller olup, bundan dolayı çoğunlukla Gauss MRF (GMRF) olarak adlandırılırlar. Diğer taraftan, Pal ve Pal (1991)'de, gri seviyesi görüntülerin histogramlarının modellenmesinde karma Poisson dağılımının kullanılmasının daha uygun olduğunu göstermiştir. Böylece bu çalışmada, basit eğiticisiz bir yapı olan Poisson MRF (PMRF) önerilmiştir. Önerilen PMRF başarımı, birçok yapay ve gerçek dünya görüntüleri üzerinde test edilmiştir. Deneysel sonuçlar hem görsel hem de sayısal olarak önerilen bu yeni yaklaşımın etkinliğini ve GMRF'ye olan üstünlüğünü göstermiştir.

Keywords

Eğiticisiz Görüntü Bölütleme Poisson Dağılımı Ma

AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS

Year 2007, Volume: 2 Issue: 4, 305 - 321, 01.05.2007

Abdülkadir Sengur

Abstract

Markov random field (MRF)-based image segmentation methods have gained considerable interest over the last few decades. The ubiquitous of MRF is the conditional model that has a joint Gaussian distribution so it is called Gaussian MRF (GMRF). On the other hand, Pal and Pal (1991), proposed that image histograms were more appropriately modeled by the mixture of Poisson distributions. Therefore, in this paper, we proposed a simple unsupervised Poisson MRF (PMRF) for gray level image segmentation. The proposed PMRF has been tested on a variety of images including artificial images and real world images. Experimental results show that by visually and by numerically comparing, it is obvious that using PMRF model generates much more accurate results than the GMRF.

Keywords

Unsupervised Image Segmentation Poisson Distribu

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Details

Primary Language Turkish
Journal Section Electrical Machines
Authors

Abdülkadir Sengur This is me

Publication Date May 1, 2007
Published in Issue Year 2007 Volume: 2 Issue: 4

Cite

APA Sengur, A. (2007). AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS. Technological Applied Sciences, 2(4), 305-321. https://doi.org/10.12739/10.12739
AMA Sengur A. AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS. Technological Applied Sciences. May 2007;2(4):305-321. doi:10.12739/10.12739
Chicago Sengur, Abdülkadir. “AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS”. Technological Applied Sciences 2, no. 4 (May 2007): 305-21. https://doi.org/10.12739/10.12739.
EndNote Sengur A (May 1, 2007) AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS. Technological Applied Sciences 2 4 305–321.
IEEE A. Sengur, “AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS”, Technological Applied Sciences, vol. 2, no. 4, pp. 305–321, 2007, doi: 10.12739/10.12739.
ISNAD Sengur, Abdülkadir. “AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS”. Technological Applied Sciences 2/4 (May 2007), 305-321. https://doi.org/10.12739/10.12739.
JAMA Sengur A. AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS. Technological Applied Sciences. 2007;2:305–321.
MLA Sengur, Abdülkadir. “AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS”. Technological Applied Sciences, vol. 2, no. 4, 2007, pp. 305-21, doi:10.12739/10.12739.
Vancouver Sengur A. AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS. Technological Applied Sciences. 2007;2(4):305-21.