Development of a Gesture Recognition System for Human-Computer Interaction
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Objectives<br> 1.3 Scope<br>2. Literature Review<br> 2.1 Gesture Recognition Techniques<br> 2.2 Computer Vision for Hand Gesture Analysis<br> 2.3 Deep Learning Models for Gesture Recognition<br>3. System Design and Architecture<br> 3.1 Image Acquisition and Preprocessing<br> 3.2 Feature Extraction and Representation<br> 3.3 Gesture Classification Models<br> 3.4 Real-time Gesture Detection<br>4. Implementation<br> 4.1 Dataset Collection and Annotation<br> 4.2 Model Training and Optimization<br> 4.3 Real-time Gesture Recognition Interface<br>5. Testing and Evaluation<br> 5.1 Performance Metrics<br> 5.2 Usability Testing<br> 5.3 Robustness and Error Analysis<br></p>
Thesis Abstract
<p> This project focuses on the development of a gesture recognition system that enables natural and intuitive interaction between users and computing devices. The system will utilize computer vision techniques, deep learning algorithms, and real-time image processing to accurately detect and interpret hand gestures for controlling applications, games, and virtual environments. The project will explore different gesture recognition approaches, optimize model performance, and evaluate the system's usability and robustness in various scenarios. <br></p>Thesis Overview
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