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
Common Tasks in Computer Vision
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- Computer Vision: Algorithms and Applications, Richard Szeliski, 2022 (Springer) DOI: 10.1007/978-3-030-34372-9 - A comprehensive textbook covering fundamental algorithms and applications in computer vision, including classification, detection, and segmentation.
- CS231n: Convolutional Neural Networks for Visual Recognition - Course Notes, Fei-Fei Li, Ehsan Adeli, Justin Johnson, Zane Durante, 2025 (Stanford University) - Provides detailed explanations of deep learning concepts and their application to computer vision tasks like image classification, object detection, and segmentation.
- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, 2012 Advances in Neural Information Processing Systems (NIPS) 25, Vol. 25 (Curran Associates, Inc.) - This paper introduced AlexNet, a breakthrough deep convolutional neural network that significantly advanced the state-of-the-art in image classification.