Calculation of Texture Features for Polluted Leaves

S.K. Badugu¹ , R.K. Kontham² , V.K. Vakulabharanam³ , B. Prajna⁴

1. Dept. of CSSE, Andhra University College of Engineering (Autonomous), Visakhapatnam, India.
2. Dept. of CSSE, Andhra University College of Engineering (Autonomous), Visakhapatnam, India.
3. Dept. of CSE, JNTU Hyderabad, Hyderabad, India.
4. Dept. of CSSE, Andhra University College of Engineering (Autonomous), Visakhapatnam, India.

Correspondence should be addressed to: suneel_it@hotmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.11-21, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.1121

Online published on Feb 28, 2018

Copyright © S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This paper discuss about polluted leaves texture features. Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation , bio-medical image processing and remote sensing. Now a day’s pollution is increasing day to day. classification of polluted leaves essential for some applications. To get the leaf based features image processing techniques are applied on the image of leaf. So texture features like mean, median ,skewness, kurtosis,GLCM and RMS of polluted leaves are published in this Article .Not only that Histogram of polluted leaves were published. Histograms of polluted leaves compared with data distribution measures like skewness and kurtosis.Manual polluted leafs identification task is time consuming process. Automatic detection of pollutant leaves is an active research topic now a days. Automatic polluted leaf identification will save time.In this paper histograms of nearly 30 polluted leafs of seven statistical measures were published.

Key-Words / Index Term :
Texture Features, Classification, Pollution, Histogram

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