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

Aggregates Classification01:29

Aggregates Classification

391
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
391
Classification of Systems-I01:26

Classification of Systems-I

319
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
319
Classification of Systems-II01:31

Classification of Systems-II

242
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
242
Force Classification01:22

Force Classification

1.7K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.7K
Classification of Signals01:30

Classification of Signals

925
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
925
Classification of Leukocytes01:30

Classification of Leukocytes

2.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.8K

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

Evolving Research Hotspots and Emerging Trends in Atopic Dermatitis and Inflammatory Bowel Disease: A Bibliometric Analysis.

Clinical, cosmetic and investigational dermatology·2026
Same author

Global research trends on the aryl hydrocarbon receptor in atopic dermatitis: a bibliometric and visualized analysis.

Frontiers in medicine·2026
Same author

Fabrication of Structured Surface Functional Layers for Enhanced Performance of Ag<sub>2</sub>Se-Based Photothermoelectric Detectors.

Micromachines·2026
Same author

Single-Mode Capability Enhancement of Curved Sapphire Fiber Utilizing High-Order Mode Suppression Characteristics Applied at High Temperature.

Micromachines·2026
Same author

IGF2BP3 promotes glycolysis of osteosarcoma stem cells by reading RNA m⁶A to stabilize ENO1 and promote malignant biological behavior of osteosarcoma.

Cell biology and toxicology·2026
Same author

Small-bore vs. large-bore chest tubes for pneumothorax: a retrospective study.

BMC surgery·2026

相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

基于CNN的多功能面部复杂性分类算法

Xiyuan Cao1, Delong Zhang1, Chunyang Jin1

  • 1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan 030051, China.

Biomimetics (Basel, Switzerland)
|June 25, 2025
PubMed
概括

准确地分类面部肤色,一个健康指标,是一个挑战. 使用卷积神经网络 (CNN) 的新型多功能深度学习算法显著提高了分类准确性,最好的结果达到97.78%.

科学领域:

  • 计算机视觉 计算机视觉
  • 医学成像分析 医学成像分析
  • 机器学习 机器学习

背景情况:

  • 面部肤色的变化可能表明潜在的健康问题.
  • 微妙的面部特征区别使肤色的准确分类变得困难.
  • 卷积神经网络 (CNN) 显示出对图像分析任务的前景.

研究的目的:

  • 开发和评估新的多功能面部肤色分类算法.
  • 通过深度学习提高面部肤色分析的准确性和效率.
  • 确定面部最佳感兴趣区域 (ROI) 和特征提取策略.

主要方法:

  • 提出了三种不同的基于CNN的算法:多功能融合,拼接和独立训练.
  • 从特定的面部ROI (鼻子,额头,阴茎,脸) 中提取和利用特征.
  • 在721张预处理的面部图像数据集上训练并验证了算法.

主要成果:

  • 多功能融合和拼接算法分别实现了95.98%和93.76%的准确性.
  • 结合多功能CNN和机器学习的最佳方法达到了97.78%的准确性.
  • 投资回报特征 (鼻子,额头,阴茎,脸) 的排列证明是最佳的分类.
关键词:
在美国,CNN是CNN.面部肤色的分类 面部肤色的分类机器学习是机器学习.多功能的多功能.

更多相关视频

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.3K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

相关实验视频

Last Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.3K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

结论:

  • 多功能深度学习算法,特别是基于融合的方法,显著优于单图像分析 (例如,EfficientNet的89.37%).
  • 从多个面部区域的特征的战略组合和排列对于高精度的肤色分类至关重要.
  • 这些发现为面部肤色分类和健康监测的深度学习应用提供了新的研究途径.