Introduction to Computer Vision
Chapter 1: Getting Started with Computer Vision
What is Computer Vision?
How Computers Interpret Images
A Brief History Overview
Common Tasks in Computer Vision
Components of a CV System
Setting Up Your Development Environment
Hands-on: Loading and Displaying an Image
Quiz for Chapter 1
Chapter 2: Digital Image Fundamentals
Pixels: The Building Blocks of Images
Digital Image Representation
Introduction to Color Spaces
Common Image File Formats
Understanding Image Resolution
Image Coordinate Systems
Reading and Writing Image Files
Practice: Exploring Image Properties
Quiz for Chapter 2
Chapter 3: Basic Image Processing Techniques
Point Operations: Brightness Adjustment
Point Operations: Contrast Adjustment
Image Histograms Explained
Histogram Equalization for Contrast Enhancement
Geometric Transformations: Scaling
Geometric Transformations: Rotation and Translation
Introduction to Image Filtering
Basic Smoothing Filters
Hands-on: Applying Basic Image Transformations
Quiz for Chapter 3
Chapter 4: Introduction to Feature Detection
What Are Features in an Image?
Why Detect Features?
Edge Detection Concepts
The Sobel Operator for Edge Detection
The Canny Edge Detector
Corner Detection Concepts
The Harris Corner Detector Idea
Introduction to Feature Descriptors
Hands-on: Detecting Edges in Images
Quiz for Chapter 4
Chapter 5: Introduction to Object Recognition
The Concept of Object Recognition
Template Matching Explained
How Template Matching Works
Limitations of Template Matching
Challenges in Object Recognition
Overview of Machine Learning Approaches
Practice: Simple Template Matching
Quiz for Chapter 5
The Concept of Object Recognition
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- Computer Vision: Algorithms and Applications, Richard Szeliski, 2022 (Springer) - This foundational textbook systematically covers computer vision, including object recognition principles, various approaches, and challenges.
- Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce, 2011 (Pearson Education) - Provides a comprehensive introduction to computer vision, discussing object recognition problems, features, and methods from a modern perspective.
- CS231n: Convolutional Neural Networks for Visual Recognition, Lecture 1: Introduction to Computer Vision, Fei-Fei Li, Justin Johnson, Serena Yeung, 2017 Stanford University Course Material - This introductory lecture outlines the field of computer vision, its historical context, primary problems like object recognition, and common challenges.