Document Details

Document Type : Article In Journal 
Document Title :
Ensembles of Normalization Techniques to Improve the Accuracy of Otsu Method
Ensembles of Normalization Techniques to Improve the Accuracy of Otsu Method
 
Subject : Computer Science 
Document Language : English 
Abstract : Otsu method is a global thresholding method that uses between class variance as a discriminant criterion in order to maximize the separation between background and foreground of an image. However, there are problems of biasness in Otsu method. These problems are caused by the differences in class variances. The threshold value obtained by Otsu method will be bias towards the larger class variance component. Hence, in this paper, a new variant of Otsu method by using normalization techniques and their ensembles is proposed. By using normalization techniques, grey level values will be transformed into a smaller range in feature space and this will affect Otsu method as this method depends on grey level values. The domination of certain grey level values also will be eliminated. Rank filtering has been applied to eliminate noises and ensemble approaches of normalization techniques are utilized to increase the performance of the proposed method. Ensemble approaches namely Maximum Variance, Majority Voting, Product Rule, Addition Rule and Average Rule have been applied on the binary images obtained. From the experiment on 20 retinal images randomly selected in 50 runs from DRIVE and STARE databases, Maximum Variance shows the most significant result that is 95.39% accuracy. While from the experiment on 15 document images randomly selected in 50 runs from DIBCO2009 and DIBCO2011 databases, Average Rule shows the most significant result that is 97.17% accuracy. This indicates the use of ensembles of normalization techniques can give promising result to improve Otsu method. 
ISSN : 1312-885X 
Journal Name : Applied Mathematical Sciences 
Volume : 9 
Issue Number : 32 
Publishing Year : 1436 AH
2015 AD 
Article Type : Article 
Added Date : Monday, March 7, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Anton Satria PrabuwonoSatria Prabuwono, Anton ResearcherDoctorateantonsatria@eu4m.eu

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