IMAGE SEGMENTATION USING SELF-ORGANIZING MAPS AND GRAY LEVEL CO-OCCURRENCE MATRICES

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Atıf İçin Kopyala

DEMİRHAN A., GÜLER İ.

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.25, sa.2, ss.285-291, 2010 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 25 Sayı: 2
• Basım Tarihi: 2010
• Dergi Adı: JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
• Sayfa Sayıları: ss.285-291
• Gazi Üniversitesi Adresli: Evet

Özet

Image segmentation is the separation of an image into segments called classes or subsets, according to one or more characteristics or features, and enhancing areas of interest by separating them from the background and other areas. Image segmentation is the most difficult stage in image processing. The success of subsequent image analysis and related applications depends greatly on the success of image segmentation. In this study images were segmented using self-organizing map (SOM) networks, and gray level co-occurrence matrices (GLCM). The performances of these methods on image segmentation were evaluated. It is seen that these methods showed %90 success on image segmentation applications.