Image Filtering, Convolution, and Edge Detection

Explore image processing techniques in this comprehensive lecture on image filtering, convolution, and edge detection. Delve into analytical and discrete derivatives, two-dimensional derivatives, and their application to images. Learn about correlation, convolution, and image noise before examining Gaussian filters and their 2D counterparts. Practice linear filtering techniques and discover image sharpening methods. Investigate the origins and characteristics of edges, including intensity profiles and the trade-off between smoothing and localization. Study various edge detection algorithms, such as Prewitt, Sobel, Marr-Hildreth, and Canny edge detectors. Understand the concept of zero crossings, separability of Gaussian and LOG filters, and evaluate edge quality. Gain valuable insights from Dr. Mubarak Shah of the University of Central Florida in this comprehensive 71-minute lecture on fundamental image processing concepts.

Intro
Definitions
Examples: Analytic Derivatives
Discrete Derivative: Finite Difference
Derivatives in 2 Dimensions
Derivatives of Images
Correlation & Convolution
Image Noise
Gaussian Filter
2-D Gaussian
Practice with linear filters
Sharpening
Edge detection
Origin of Edges
What is an Edge?
Characterizing edges
Intensity profile
Tradeoff between smoothing and localization
Edge Detectors
Prewitt and Sobel Edge Detector
Prewitt Edge Detector
David Marr
Ellen Hildtreh
Marr Hildreth Edge Detector
Finding Zero Crossings
On the Separability of Gaussian
On the Separability of LOG
LOG Algorithm
Quality of an Edge
John Canny
Canny Edge Detector