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Image processing methodologies and domain is quite wide and really efficient now days for real time applications. Our work primarily deals with the domain of image segmentation and using segmentation concept, texture recognition has been performed with comparative results and simulations performed over a particular image dataset. The initial work in our proposed work is to perform segmentation on each part image then performing extraction .We have focused on segmentation followed by extraction so that the classification result may not contain much error. The conventional approach has been implemented in this regard first and then the main problem that has been formulated is patch up data pixels together which provide error in getting right and appropriate texture. In order to deal with the problem formulated in the existing work we have proposed a new commuted method in which the extraction and segmentation of image depends on the dynamic threshold set by user.
Divya Mathur. 2019. \u201cA Collaborative approach for segmentation of probe image for efficient texture recognition\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 19 (GJCST Volume 19 Issue C1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 102
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Divya Mathur, Sandeep Upadhyay (PhD/Dr. count: 0)
View Count (all-time): 244
Total Views (Real + Logic): 5195
Total Downloads (simulated): 1362
Publish Date: 2019 04, Tue
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Image processing methodologies and domain is quite wide and really efficient now days for real time applications. Our work primarily deals with the domain of image segmentation and using segmentation concept, texture recognition has been performed with comparative results and simulations performed over a particular image dataset. The initial work in our proposed work is to perform segmentation on each part image then performing extraction .We have focused on segmentation followed by extraction so that the classification result may not contain much error. The conventional approach has been implemented in this regard first and then the main problem that has been formulated is patch up data pixels together which provide error in getting right and appropriate texture. In order to deal with the problem formulated in the existing work we have proposed a new commuted method in which the extraction and segmentation of image depends on the dynamic threshold set by user.
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