基于SVM和组合优化模型的手势识别  

Study on Hand Gesture Recognition and Portfolio Optimization Model Based on SVM

作　　者：蔡芝蔚[1] 吴淑燕[1] 宋俊锋[1] 

机构地区：[1]丽水学院工程与设计学院,浙江丽水323000

出　　处：《系统仿真学报》2016年第8期1812-1817,共6页Journal of System Simulation

基　　金：丽水市科技局公益项目(2013JYZB19)

摘　　要：针对手势识别进行了相关研究,通过采用机器学习中的SVM(Support Vector Machine)算法对手势识别中的有关特征进行了提取,再利用ANN(Artifical Neural Network)、HMM(Hidden Markov Model)和DTW(Dynamic Time Wrapping)三种手势识别算法的组合优化思想对手势进行识别。实验结果表明,组合优化模型的手势识别方法具有较好的准确率,是一种有效的手势识别方法。Hand gesture recognition was researched. The idea of extracting related features was proposed by using SVM algorithm in machine learning domain, and combination optimization method was used, which consists of ANN, HMM and DTW, to do hand gesture recognition. The experimental results show that portfolio optimization model based gesture recognition method has high accuracy and is very effective.

关 键 词：手势识别 支持向量机 组合优化 特征提取 虚拟现实 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]