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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

906
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...
906

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相关实验视频

Updated: Jul 9, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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使用人工智能辅助的纹理特征进行手写识别和验证.

Heng Zhao1, Huihui Li2

  • 1College of Infommation Engineering and Artificial Inteligence, Zhengzhou Vocational University of Information and Technology, Zhengzhou, 450046, China. zhaoheng188@outlook.com.

Scientific reports
|December 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一个空间变化依赖验证 (SVV) 方案,用于验证数字和手写签名的真实性. 该方法使用纹理特征和卷积神经网络来提高精度并减少智能控制系统中的错误阳性.

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相关实验视频

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科学领域:

  • 计算机科学 计算机科学
  • 自动化工程自动化工程
  • 生物识别信息 生物识别信息

背景情况:

  • 智能过程控制和自动化系统需要强大的签名验证.
  • 由于外部因素,数字手写签名显示像素强度和空间布局的变化.
  • 现有的验证方法与这些固有的波动作斗争.

研究的目的:

  • 引入一个新的空间变化依赖验证 (SVV) 计划.
  • 提高自动化系统中签名认证的准确性和可靠性.
  • 为了应对像素强度和数字签名空间变化的挑战.

主要方法:

  • 使用纹理特征 (TF) 进行签名验证.
  • 采用卷积神经网络 (CNN) 进行分层分析.
  • 识别和空间绘制签名点以检测纹理特征匹配.
  • 在连续的识别点之间提取纹理特征,以最大限度地减少假阳性.

主要成果:

  • 通过分析像素强度和空间变化,SVV方案证明了有效的验证.
  • 卷积神经网络成功生成了新的识别点,并选择了最大匹配特征.
  • 该方法通过避免对不同的纹理进行代验证,显著减少了假阳性.
  • 使用包括准确性,精度,纹理检测,假阳性和验证时间在内的指标验证了性能.

结论:

  • 拟议的空间变化依赖验证 (SVV) 方案为验证数字和手写签名提供了一种可靠的方法.
  • 纹理特征的整合和CNN有效地处理签名的变化.
  • 这种方法提高了智能过程控制和自动化系统的安全性和可靠性.