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- W2537433346 abstract "Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images." @default.
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- W2537433346 date "2011-09-01" @default.
- W2537433346 modified "2023-09-26" @default.
- W2537433346 title "Classification of Ordered Texture Images Using Regression Modelling and Granulometric Features" @default.
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- W2537433346 doi "https://doi.org/10.1109/imvip.2011.20" @default.
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