Unsupervised Image Histogram Peak Detection Based on Gaussian Mixture Model

被引：0
|
作者：
Zheng, Yingping [1 ]
Li, Zhijiang [1 ]
Cao, Liqin [1 ]
机构：
[1] Wuhan Univ, Sch Printing & Packaging, Wuhan, Hubei, Peoples R China
来源：
APPLIED SCIENCES IN GRAPHIC COMMUNICATION AND PACKAGING | 2018年 / 477卷
基金：
中国国家自然科学基金;
关键词：
Gaussian mixture model; Truncation data problem Expectation maximization algorithm; Least squares regression; ALGORITHM;
D O I：
10.1007/978-981-10-7629-9_28
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Image histogram peak detection is a fundamental technique in digital image processing and relative areas. It has been found that Gaussian mixture model is an effective method to obtain the histogram peaks. However, how to set cluster centers and fit truncation data remain problems that deserve to be explored further. To solve the latter problem, this paper proposes a method consisting of data prediction, unsupervised data fitting and peaks acquisition. Extensive experiments are carried out to demonstrate the performance, and the results prove that our method can improve stability, deal with truncation data, and adaptively find histogram peaks.
引用
收藏
页码：233 / 241
页数：9
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