Low-level computer vision techniques for processing of extensive air shower track images

被引：0

作者：

Vrabel, Michal ^{[1
]}

Genci, Jan ^{[1
]}

Bobik, Pavol ^{[2
]}

机构：

[1] Tech Univ Kosice, Fac Elect Engn & Informat Technol, Letna 9, Kosice 04001, Slovakia

[2] Slovak Acad Sci, Inst Expt Phys, Watsonova 47, Kosice 04001, Slovakia

来源：

2017 IEEE 14TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATICS | 2017年

关键词：

PERFORMANCE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An extensive air shower can be observed as a bright spot moving through the field of view of an orbital fluorescence detector. A challenging part of the air shower recognition is segmentation of its track. The issues arise from a low signal to noise ratio. This paper provides a short review of selected low-level computer vision techniques such as filtering and thresholding methods, which are for a demonstration applied to a composite simulated air shower image. The article should provide a shortlist of algorithms that can be applied as a part of more complex event classification or reconstruction procedure.

引用

页码：425 / 430

页数：6

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