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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Infrared (IR) Spectroscopy: Overview01:09

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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相关实验视频

Updated: Jul 15, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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基于图形采样的多流增强网络可见红外人重新识别.

Jinhua Jiang1, Junjie Xiao1, Renlin Wang2

  • 1College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括

这项研究引入了一个新的可见红外人重新识别 (VI Re-ID) 网络,该网络使用轮信息和图形采样来提高准确性. 基于图表采样的多流增强网络 (GSMEN) 有效地减少了模式差异,以更好地匹配人.

关键词:
轮扩展模块 轮扩展模块交叉模式图表采样器采样器多模式数据多模式数据第六章 重新识别模式差异的差异性

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 单模个人重新识别 (Re-ID) 不足以满足全天的检索需求.
  • 多模式数据,特别是可见红外线人重新识别 (VI Re-ID),至关重要,但受到重大模式差异的挑战.
  • 现有的方法往往忽略了人的轮信息的模式不变性.

研究的目的:

  • 解决VI Re-ID中的挑战,特别是模式差异和区分硬样本.
  • 提出一个新的网络,利用轮信息和先进的采样技术来改善跨模式匹配.
  • 为了提高人匹配在可见和红外模式的稳定性和准确性.

主要方法:

  • 提出了基于图形采样的多流增强网络 (GSMEN),其中包括一个轮扩展模块 (CEM).
  • CEM集成了人形轮信息,以减少模式差异,增强匹配稳定性.
  • 引入了交叉模式图谱采样器 (CGS) 进行智能样本选择,将类似的交叉模式样本分组起来,以探索硬类边界.

主要成果:

  • 对SYSU-MM01和RegDB数据集的实验证明了拟议的GSMEN的优越性.
  • 在RegDB数据集上的VIS→IR任务中实现了93.69%的Rank-1和92.56%的mAP.
  • 该方法有效地减少了模式差异,并改善了硬交叉模式样本的区分.

结论:

  • 拟议的GSMEN通过利用轮信息有效地解决了VI Re-ID中的模式差异挑战.
  • 交叉模式图表采样器通过专注于困难的交叉模式样本对来帮助培训.
  • 该方法显示了显著的性能改进,突出了VI Re-ID.中的轮和智能采样的重要性.