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1
Intro
2
Definitions
3
Examples: Analytic Derivatives
4
Discrete Derivative: Finite Difference
5
Derivatives in 2 Dimensions
6
Derivatives of Images
7
Correlation & Convolution
8
Image Noise
9
Gaussian Filter
10
2-D Gaussian
11
Practice with linear filters
12
Sharpening
13
Edge detection
14
Origin of Edges
15
What is an Edge?
16
Characterizing edges
17
Intensity profile
18
Tradeoff between smoothing and localization
19
Edge Detectors
20
Prewitt and Sobel Edge Detector
21
Prewitt Edge Detector
22
David Marr
23
Ellen Hildtreh
24
Marr Hildreth Edge Detector
25
Finding Zero Crossings
26
On the Separability of Gaussian
27
On the Separability of LOG
28
LOG Algorithm
29
Quality of an Edge
30
John Canny
31
Canny Edge Detector
Description:
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.

Image Filtering, Convolution, and Edge Detection

University of Central Florida
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