Kernel-Based Edge-Preserving Methods for Abrupt Change Detection

被引:3
作者
Xiang, Shiming [1 ]
Tang, Bo [1 ]
机构
[1] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
关键词
Kernel; Microsoft Windows; Image edge detection; Euclidean distance; Signal processing algorithms; Probability distribution; Change detection algorithms; Abrupt change detection; edge preserving; bilateral filter; event detection; CHANGE-POINT DETECTION; TIME;
D O I
10.1109/LSP.2019.2957645
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Abrupt change detection is critical to monitor the occurrence of abnormal events from sensor data for situational awareness of complex systems. However, various disturbances and noises applied to the data observations may pose significant challenges to the robustness of many abrupt change detection methods. Recent researches have shown that bilateral filter can acquire outstanding performance on removing noises from images while preserving edge information. In this letter, we propose two improved edge-preserving memory-based cumulative sum (MB-CUSUM) methods that are able to make the abrupt change detection method more robust against noises. Our experimental studies show that the proposed methods can achieve superior performance over state-of-the-art methods to detect abrupt changes, which demonstrates the effectiveness and feasibility of their practical use.
引用
收藏
页码:86 / 90
页数:5
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