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
Why Detect Features?
Was this section helpful?
- Computer Vision: Algorithms and Applications, Richard Szeliski, 2010 (Springer) DOI: 10.1007/978-1-84882-935-0 - This book provides a comprehensive foundation for computer vision, explaining the motivations and applications of feature detection methods in detail. This is the author's draft, widely considered the updated 2nd edition.
- Multiple View Geometry in Computer Vision, Richard Hartley, Andrew Zisserman, 2003 (Cambridge University Press) DOI: 10.1017/CBO9780511811685 - While advanced, this foundational book extensively uses features for camera calibration, 3D reconstruction, and image matching, illustrating their necessity for robust geometric tasks.
- Distinctive Image Features from Scale-Invariant Keypoints, David G. Lowe, 2004 International Journal of Computer Vision, Vol. 60 (Springer) DOI: 10.1023/B:VISI.0000029664.99615.94 - This foundational paper introduces SIFT, but importantly, it details the limitations of previous methods and the necessity of features that are robust to scale, rotation, and illumination changes, directly aligning with the section's core topic.