Finding Meaningful Detections: False Discovery Rate Control in Correlated Detection Maps
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)(2021)
摘要
The detection of faint sources is a key step in several areas of signal and image processing. The reliability of the detection depends on two key components: (i) the detection criterion used to derive detection maps in which the signature of a source takes the form of a detection peak, and (ii) the extraction procedure identifying the meaningful detections. In this work, the expected false discovery rate guides the selection of meaningful detections. A procedure is designed to account for correlations in the detection maps. This prevents the issue of the multiple detections of a single source and corrects the number of effective independent tests performed. The proposed approach is evaluated on an astrophysical application: the detection of exoplanets by high-contrast imaging.
更多查看译文
关键词
detection,FDR,correlated data,matched filter
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要