Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

454
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
454
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Assessing Color Inconstancy of CAD/CAM Materials: Influence of CIE White LED Illuminants and Substrate Shade.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]·2026
Same author

Multiscale RGB-Guided Fusion for Hyperspectral Image Super-Resolution.

Journal of imaging·2026
Same author

Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC).

Scientific data·2025
Same author

Database of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and hyperspectral imaging (HSI) spectra of pigments and dyes for historical document analysis.

Analytical and bioanalytical chemistry·2025
Same author

A Convolutional Framework for Color Constancy.

IEEE transactions on neural networks and learning systems·2024
Same author

Temperature distribution inversion in infrared multispectral imaging based on ensemble network.

Optics letters·2024
Same journal

Exploring peroxidase substrates driven etching of gold nanorods towards indirect detection of amyloid beta<sub>1</sub><sub>-</sub><sub>42</sub>.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

A highly efficient and interpretable framework for high-precision lithological identification integrating SERS and XGBoost.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

Blue to cold white tunable emission via Eu<sup>2+</sup> → Tb<sup>3+</sup> energy transfer in [Na<sup>+</sup>-Tb<sup>3+</sup>] co-substituted Sr<sub>5</sub>(PO<sub>4</sub>)<sub>3</sub>Br: Eu<sup>2+</sup> phosphors.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

Unlocking HClO detection via an AIE "off-on-off" fluorescent switch.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

Activatable fluorescent probe for auxiliary diagnosis of atherosclerosis: from foam cells to clinical samples.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

Spectroscopic studies on human androglobin regulated by its heme distal Gln12 and IQ motif via interaction with calmodulin.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

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

Published on: June 18, 2021

2.3K

在历史文档中使用超光谱成像和机器学习方法进行墨水分类.

Ana Belén López-Baldomero1, Marco Buzzelli2, Francisco Moronta-Montero1

  • 1Department of Optics, University of Granada, Faculty of Sciences, Campus Fuentenueva, s/n, Granada, 18071, Spain.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|March 6, 2025
PubMed
概括
此摘要是机器生成的。

超光谱成像和机器学习准确地识别历史油墨,即使有退化. 一个深度学习模型实现了98%的F1得分,有助于手稿的保存.

关键词:
文化遗产 文化遗产 文化遗产数据融合数据融合历史文件 历史文件超光谱成像技术的使用.墨水分类 墨水分类 墨水分类机器学习方法的机器学习方法.材料识别 材料识别

更多相关视频

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

7.9K
Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

12.4K

相关实验视频

Last Updated: May 24, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

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

Published on: June 18, 2021

2.3K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

7.9K
Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

12.4K

科学领域:

  • 分析化学 分析化学
  • 影像科学 影像科学
  • 计算科学 计算科学

背景情况:

  • 由于降解和光谱重叠,墨水识别具有挑战性.
  • 超光谱成像为光谱分析提供了一种非侵入性的方法.
  • 机器学习可以对复杂的光谱数据进行分类.

研究的目的:

  • 使用超光谱成像,对金属酸盐,含碳和不含碳的油墨进行分类.
  • 评估传统和深度学习模型的墨水分类准确性.
  • 评估历史文档的非侵入性墨水分析的可行性.

主要方法:

  • 在多个系统中利用过光谱成像.
  • 应用了六种监督机器学习模型:SVM,KNN,LDA,RF,PLSDA和一个DL模型.
  • 综合数据融合,样本提取,基础真相创建和后处理.

主要成果:

  • 所有模型都在模拟样本上实现了>90%的微平均精度.
  • 深度学习模型获得了最高的F1分数 (98%).
  • 传统模型在历史案例研究文件上表现更好.

结论:

  • 超光谱成像与机器学习相结合,有效用于非侵入性墨水识别.
  • 这种方法即使在降解材料和光谱重叠的情况下也是可靠的.
  • 这项技术支持保存和分析历史手稿.