Computer Vision: Edge Detection Techniques - Part 1 - Lecture 4

Explore edge detection techniques in computer vision through this comprehensive lecture from the University of Central Florida's CAP5415 course. Delve into the origins and types of edges, understanding their importance in image processing. Examine intensity profiles and the effects of Gaussian noise on edge detection. Learn about smoothing techniques and the derivative theorem of convolution as solutions to noise-related challenges. Evaluate various edge detection methods, including the Prewitt and Sobel edge detectors, while considering design criteria for effective edge detection algorithms. Gain insights into the evolution of boundary detection over 45 years of research and development in the field of computer vision.

Intro
Outline
Origins of Edges
Types of edges
Why edge detection?
Closeup of edges
Characterizing edges
Intensity profile
With a little Gaussian noise
Effects of Noise
Solution: smooth first
Derivative theorem of convolution
Solution: Smoothing
Evaluate Edge Detection
Design Criteria for Edge Detection
45 years of boundary detection
Prewitt and Sobel Edge Detector