文章基本信息

标题：PROBABILISTIC NEURAL NETWORK � A BETTER SOLUTION FOR NOISE CLASSIFICATION
本地全文：下载
作者：T. SANTHANAM ; S. RADHIKA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2011
卷号：27
期号：1
出版社：Journal of Theoretical and Applied
摘要：Classification is one of the common tasks of human behavior. Classification problems arise when an entity needs to be assigned into a predefined set based on a number of features associated with that entity. Neural Network models prove to be a competitive alternative to traditional classifiers for many practical classification problems. Noise classification in digital image processing is a must so as to identify the suitable filters for smoothing the image for further processing. The use of Probabilistic Neural Network to classify the noise present in an image after extracting the statistical features like skewness and kurtosis is explored in this article. When the noises are classified accurately, identification of the filter becomes an easy task.
关键词：Probabilistic Neural Network; Noise Classification; Statistical Features