Khatun, Mahmuda and Gray, Alison and Marshall, Stephen (2011) Classification of ordered texture images using regression modelling and granulometric features. In: Irish Machine Vision and Image Processing Conference, 2011-09-08 - 2011-09-09, Dublin.
imvip_ieee_5.pdf - Published Version
Download (205kB) | Preview
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.
|Item type:||Conference or Workshop Item (Paper)|
|Keywords:||granulometry, structuring, pattern spectrum, Mathematics, Probabilities. Mathematical statistics, Electrical engineering. Electronics Nuclear engineering|
|Subjects:||Science > Mathematics
Science > Mathematics > Probabilities. Mathematical statistics
Technology > Electrical engineering. Electronics Nuclear engineering
|Department:||Faculty of Science > Mathematics and Statistics
Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
|Depositing user:||Pure Administrator|
|Date Deposited:||13 Sep 2011 14:11|
|Last modified:||21 Jul 2015 12:55|
Actions (login required)