Introduction

This comprehensive course introduces participants to the core concepts and techniques of computer vision, enabling them to build foundational skills necessary for further study or professional work in the field.

Objectives

• Understand the fundamental principles of computer vision.
• Gain hands-on experience with essential computer vision libraries and tools.
• Learn techniques for image processing and feature extraction.
• Explore object detection and image classification methods.
• Implement real-world computer vision applications.

Course Outlines

Day 1: Introduction to Computer Vision

• Overview of computer vision and its applications
• History and evolution of the field
• Basic image representation and formats
• Setting up the programming environment
• Introduction to popular computer vision libraries (OpenCV, PIL)

Day 2: Image Processing Fundamentals

• Image filtering and enhancement techniques
• Color spaces and transformations
• Geometric transformations and image resizing
• Thresholding and segmentation basics
• Practical exercises using OpenCV

Day 3: Feature Detection and Extraction

• Understanding features in images
• Edge detection algorithms (Sobel, Canny)
• Corner and blob detection
• Feature descriptors (SIFT, SURF, ORB)
• Matching features between images

Day 4: Object Detection and Recognition

• Introduction to object detection concepts
• Classical methods (Haar cascades, HOG)
• Image classification basics
• Deep learning overview for vision tasks
• Hands-on: Detecting and classifying objects

Day 5: Real-World Applications and Projects

• Case studies of successful computer vision systems
• Building a simple project (face detection, barcode reading, etc.)
• Evaluation and performance metrics
• Ethical considerations in computer vision
• Course wrap-up and next steps