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Introduction

Computer vision for gesture recognition is a rapidly growing field with great potential for various applications such as human-computer interaction, sign language recognition, and virtual reality. This thesis aims to explore the use of computer vision techniques for recognizing gestures and translating them into meaningful commands.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to computer vision
2.2 Gesture recognition techniques
2.3 Deep learning for gesture recognition
2.4 Challenges in gesture recognition
2.5 Applications of gesture recognition
2.6 Existing systems for gesture recognition
2.7 Evaluation metrics for gesture recognition
2.8 Hardware requirements for gesture recognition
2.9 Software tools for gesture recognition
2.10 Recent advancements in gesture recognition

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 Feature extraction techniques
3.4 Classification algorithms
3.5 Model training and evaluation
3.6 Integration with user interface
3.7 Real-time gesture recognition
3.8 Performance optimization
3.9 User testing and feedback

Chapter 4: System Implementation
4.1 Development environment setup
4.2 Data collection and annotation
4.3 Feature extraction implementation
4.4 Model selection and training
4.5 User interface design
4.6 Real-time gesture recognition implementation
4.7 Performance evaluation
4.8 System deployment and testing

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Discussion of results
5.3 Implications for future research
5.4 Recommendations for further development
5.5 Conclusion and closing remarks

Thesis Overview

Computer vision for gesture recognition is a field of study that focuses on developing systems capable of recognizing and interpreting gestures made by humans. With the advancement of technology, computer vision techniques have been increasingly used to enable machines to understand and respond to human gestures in real-time. This thesis aims to investigate the use of computer vision for gesture recognition and develop a system that can accurately recognize and interpret a variety of hand gestures.

Chapter 1 provides an introduction to the research topic, including the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter 2 presents a comprehensive review of the existing literature on computer vision for gesture recognition, covering various techniques, challenges, applications, and recent advancements in the field.

Chapter 3 outlines the system design and methodology, including the system architecture, data collection, preprocessing, feature extraction, classification algorithms, model training, integration with user interface, real-time gesture recognition, performance optimization, and user testing. Chapter 4 details the implementation of the system, including the development environment setup, data collection and annotation, feature extraction implementation, model training, user interface design, real-time gesture recognition implementation, performance evaluation, system deployment, and testing.

Chapter 5 concludes the thesis with a summary of findings, discussion of results, implications for future research, recommendations for further development, and closing remarks. Overall, this thesis aims to contribute to the field of computer vision for gesture recognition by designing and implementing a system that can accurately recognize and interpret a wide range of hand gestures for various applications.

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