Computer Vision Fundamentals - Lesson 1: Introduction to Computer Vision

Overview:

This lesson will introduce the basic concepts of computer vision, its applications, and the key techniques used in the field. It will set the foundation for understanding how machines interpret and analyze visual data.

Objectives:

By the end of this lesson, students should be able to:

• Understand what computer vision is and its importance.
• Recognize various applications of computer vision in real-world scenarios.
• Identify the fundamental concepts and techniques in computer vision.

Topics Covered:

1. What is Computer Vision?

   • Definition and scope.
   • Historical context and evolution.
   • How it relates to artificial intelligence and machine learning.

2. Applications of Computer Vision:

   • Real-world examples:
     • Object detection and recognition (e.g., in self-driving cars).
     • Image segmentation (e.g., in medical imaging).
     • Facial recognition (e.g., in security systems).
     • Optical character recognition (OCR) (e.g., in document scanning).
   • Emerging applications:
     • Augmented reality.
     • Gesture recognition.
     • Automated video analysis.

3. Basic Concepts in Computer Vision:

   • Pixels and images: Understanding image data.
   • Image processing vs. Computer vision.
   • Key challenges in computer vision: Lighting, viewpoint, scale, and occlusion.

4. Tools and Libraries for Computer Vision:

   • Introduction to popular computer vision libraries:
     • OpenCV.
     • TensorFlow and Keras for deep learning-based approaches.
     • PyTorch.

5. Setting Up the Environment:

   • Installation of Python and necessary libraries (OpenCV, TensorFlow, PyTorch).
   • Basic setup and configuration.

This first lesson is designed to provide students with a broad understanding of computer vision, preparing them for more detailed and technical lessons to follow.