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One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segmentation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu’s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods.
V Vijaya Kumar. 2017. \u201cTexture Image Segmentation using Morphology in Wavelet Transforms\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 101
Country: India
Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision
Authors: V Vijaya Kumar (PhD/Dr. count: 0)
View Count (all-time): 282
Total Views (Real + Logic): 7023
Total Downloads (simulated): 1792
Publish Date: 2017 04, Tue
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One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segmentation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu’s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods.
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