Explore edge detection techniques in image processing through this comprehensive lecture covering various methods including Prewitt, Sobel, Marr-Hildreth, and Canny edge detectors. Learn about image derivatives, Gaussian smoothing, and evaluation metrics for edge quality. Discover the principles behind detecting discontinuities in images and understand the steps involved in popular edge detection algorithms. Gain insights into the work of David Marr and John Canny, and delve into concepts such as 2-D Gaussian, zero crossings, and non-maximum suppression.
Overview
Syllabus
Intro
An Application
Edge Detection in Images
Evaluation Metrics
What is an Edge?
Detecting Discontinuities
Image Derivatives
Derivatives and Noise
Image Smoothing
Gaussian Smoothing
Edge Detectors
Prewitt and Sobel Edge Detector
Prewitt Edge Detector
David Marr
Marr Hildreth Edge Detector
2-D Gaussian
Finding Zero Crossings
On the Separability of Gaussian
On the Separability of LOG
Seperability
Example
LOG Algorithm
Quality of an Edge
John Canny
Canny Edge Detector Steps
First Two Steps
Derivative of Gaussian
Third Step
Fourth Step
Non-Maximum Suppression
Taught by
UCF CRCV
Tags
Related Courses
-
Edge Detection Techniques - Prewitt, Sobel, Marr-Hildreth, and Canny - Lecture 4
-
Image Filtering, Convolution, and Edge Detection
-
Edge Detection in Computer Vision - Lecture 3
-
Edge Detection in Computer Vision - Part II - Lecture 7
-
Computer Vision: Edge Detection Techniques - Part 1 - Lecture 4
-
Filtering in Computer Vision - Lecture 2
Reviews
5.0 rating, based on 2 Class Central reviews
-
The course gave me confidence to do more advanced work in the subject. The course structure was clear and the expectations for student learning were defined. Thank you for a great course.
-
I really enjoyed this class and the format it was presented in. I feel that I am achieving the learning outcomes.
