Neural network based detection of heterogeneities in noisy images

被引：0
|
作者：
Abramov S. [1 ]
Naumenko A. [1 ]
Lukin V. [1 ]
Krivenko S. [1 ]
Kaluzhinov I. [1 ]
机构：
[1] National Aerospace University, Kharkiv Aviation Institute, 17 Chkalov St., Kharkiv
来源：
Lukin, V. (lukin@ai.kharkov.com) | 1691年 / Begell House Inc.卷 / 79期
关键词：
Accuracy; Heterogeneity detection; Neural network; Noisy image;
D O I：
10.1615/TELECOMRADENG.V79.I19.20
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Many methods of image processing include a stage of detecting heterogeneities (edges, small sizes objects, textures). It is often difficult to reach efficient detection due to noise presence in analyzed images when conventional detectors fail. Neural networks are tools that allow to partly improve detectability of heterogeneities due to joint use (aggregation) of elementary detectors. Performance can be improved due to proper selection of elementary detectors as well as pre-processing (pre-filtering) or post-processing (aggregation of detection results). In this paper, we consider some of aforementioned aspects and give examples of neural network learning and application to different test and real life images. © 2020 Begell House Inc.. All rights reserved.
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页码：1691 / 1705
页数：14
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