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http://dx.doi.org/10.5391/JKIIS.2012.22.5.555

An Improvement of Recognition Performance Based on Nonlinear Equalization and Statistical Correlation  

Shin, Hyun-Soo (대구가톨릭대학교 IT공학부)
Cho, Yong-Hyun (대구가톨릭대학교 IT공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.5, 2012 , pp. 555-562 More about this Journal
Abstract
This paper presents a hybrid method for improving the recognition performance, which is based on the nonlinear histogram equalization, features extraction, and statistical correlation of images. The nonlinear histogram equalization based on a logistic function is applied to adaptively improve the quality by adjusting the brightness of the image according to its intensity level frequency. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to rapidly and accurately express the similarity between the images. The local features based on independent component analysis(ICA) that is used to calculate the NCC, is also applied to statistically measure the correct similarity in each images. The proposed method has been applied to the problem for recognizing the 30-face images of 40*50 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the preprocessing, or the methods of conventional and adaptively modified histogram equalization, respectively.
Keywords
Image Recognition; Nonlinear Histogram Equalization; Similarity criterion; Normalized Cross-correlation Coefficient; Independent Component Analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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