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Updated: Jun 15, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

根据重建的高光谱图像检测.

Ali Mohammed Ridha^1,2, Nor Ashidi Mat Isa², Ayman Tawfik¹

¹Department of Electrical and Computer Engineering, Ajman University, Ajman P.O. Box 346, United Arab Emirates.

Journal of imaging

|August 28, 2024

概括

一个新的系统使用从普通RGB照片中重建的高光谱图像来检测. 这种具有成本效益的方法在没有昂贵的设备的情况下对诊断皮肤疾病充满希望.

科学领域:

皮肤病学和医学成像学

背景情况:

一般性影响超过85%的青少年和成年人,影响生活质量.
超光谱成像 (HSI) 对于皮肤状况诊断是有效的,但受到高设备成本的限制.

研究的目的:

开发一种新的,具有成本效益的检测系统,使用重建的超光谱 (HS) 图像.
基于重建的HS图像来评估检测算法的性能.

主要方法:

使用先前开发的HS重建模型创建了重建的HS图像数据集.
引入了一个新的检测算法,将重建的HS图像与RetinaNet架构集成在一起.

主要成果:

使用重建的HS图像的拟议算法超过了仅基于RGB图像的方法.
重建的HS图像为检测提供了专门的HSI设备的可行,负担得起的替代方案.

结论:

重建的超光谱成像为检测提供了一个有希望的,低成本的方法.
这项技术有可能扩展到诊断其他疾病,从而提高了先进成像技术的可用性.

关键词:

网膜网 (RetinaNet) 是一个网膜网.的检测检测的检测超光谱成像技术的使用.超光谱重建的重建机器学习是机器学习.

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