摘要
该文面向本科实践教学,以静态手势识别为目标,利用机器学习开发了机器视觉实验项目。在实验的算法设计部分,分别利用支持向量机和卷积神经网络实现了两种手势识别算法。文中详细介绍了两种手势识别算法的原理、特点和流程。在实验的算法验证部分,详细介绍了利用Python语言、编译环境和数据集实现算法仿真的方法。仿真结果显示,两种算法均可有效识别多种静态手势。该实验项目包括任务分析、算法设计、算法优化、编程实现、结果分析等环节。通过本实验的训练,有助于提升本科生在智能图像处理领域的实践能力。
A machine vision experiment project is developed by the use of machine learning in this paper.It is intended for undergraduate practical teaching and aims at static gesture recognition.In the algorithm design part of the experiment,support vector machine and convolutional neural network are used separately to implement two gesture recognition algorithms.In addition,the principles,characteristics and processes of two gesture recognition algorithms are introduced in detail.In the algorithm validation part of the experiment,a detailed introduction is given to the method of implementing algorithm simulation using Python language,compilation environment,and datasets.The simulation results show that both algorithms can effectively recognize multiple static gestures.This experimental project includes task analysis,algorithm design,algorithm optimization,programming implementation,and result analysis.Through the training of this experiment,it is helpful to enhance the practical ability of undergraduate students in the field of intelligent image processing.
作者
白煜
李香萍
张雨菡
BAI Yu;LI Xiangping;ZHANG Yuhan(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《实验技术与管理》
CAS
北大核心
2023年第6期187-191,198,共6页
Experimental Technology and Management
关键词
机器视觉
卷积神经网络
支持向量机
手势识别
machine vision
convolutional neural network
support vector machine
gesture recognition
