Static Gesture Recognition Based on HCbCr and WHS Model

目录

声明

Abstract

摘要

Acknowledgments

Table of Contents

List of Figures

List of Tables

Chapter 1 Introduction

1．1Background and Research Significance

1．2Research Status

1．3A Brief Analysis of Overview

1．4The Research Content and Arrangement of the Paper

1．4．1Main work of the thesis

1．4．2Chapter Arrangement of this Thesis

Chapter 2 Skin Color Detection

2．1 Skin Color Detection Based on HSV Color Space

2．1．1 HSV Color Space

2．1．2 HSV Color Space Conversion Algorithm

2．1．3 HSV Color Space Model

2．2 Skin Color Detection Based on Y-Cb-Cr Color Space

2．2．1 Y-Cb-Cr Color Space

2．2．2 YCbCr Color Space Conversion Algorithm

2．2．3 YCbCr Color Space Skin Color Model

2．3Skin Color Detection Model Based on H-Cb-Cr

2．3．1 Accuracy of Skin Color Model

2．3．2 Comparison of Skin Color Models Based on H Value and CbCr Value

2．3．3 Skin Color Model H-Cb-Cr

2．3．4 Analysis of Skin Color Detection Results Based on H-Cb-Cr

2．4Summary of this Chapter

Chapter 3 Image segmentation

3．1Mathematical Morphology

3．1．1Introduction of Morphology

3．1．2Basic Operation of Morphology

3．1．3Application of Mathematical Morphology

3．2Contour Acquisition Based on Watershed Algorithm

3．2．1Introduction of watershed algorithm

3．2．2Contour Acquisition of Watershed Algorithm Based on Marker

3．2．3Experiment and Analysis

3．3Region Framing Based on Seed Filling Algorithm

3．3．1Introduction of Seed Filling Algorithm

3．3．2Region Framing Based on Seed Filling Algorithm

3．3．3Experiment and Analysis

3．4Summary of this Chapter

Chapter 4 Gesture Tracking Model

4．1Gesture Region Tracking Model

4．2Area Length and Width

4．3Skin Color Ratio

4．4Gesture Region Tracking Model Based on Height-Width-Sr

4．5 Experiment and Analysis

Chapter 5 Gesture Recognition Based on Machine Learning

5．1 Machine Learning Classification

5．2Description of Geometric Eigenvectors

5．2．1Boundary Description

5．2．2Area Description

5．3Moment of Image

5．3．1The Concept of Moment

5．3．2Hu Moment Invariants

5．4Support Vector Machine

5．4．1Gesture Target Classification Method

5．4．2SVM Kernel Function

5．5Data Processing

5．5．1Building Gesture Samples Database

5．5．2Gesture Images Preprocessing

5．5．3 Hu Moment Feature Extraction

5．5．4Training SVM Model

5．6Experiment and Analysis

Chapter 6 Conclusion

6．1Research Summary

6．2Main Innovations of this Paper

6．3Further Work

References