| Summary: | 碩士 === 國立臺灣科技大學 === 資訊管理系 === 99 === Histogram equalization (HEQ) is a technology for improving the darkness and the brightness of the image by adjusting the gray levels based on the cumulative distribution function (CDF). In recent years, this method has been applied to different issues, including robust speech recognition for solving the mismatch between the noisy speech and the clean speech, and natural language processing for the cross-database problem.
This paper analyzed how histogram equalization may influence a simple classification problem by simulation. The results showed the rough curve of CDF caused by insufficient data would lead to the poor mapping between training and test data and degrade the performance. Direct and indirect operations of histogram equalization achieve similar performance for linear or non-linear transformation, while the performance of the indirect one is more sensitive to type of classifiers. With sufficient amount of training data, HEQ and mean-standard deviation weight (MSW) can achieve compatible performances for linear transformation, while HEQ appears superior for nonlinear transformation.
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