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1
Intro
2
Image Feature Extraction
3
Shape Features
4
How to Fit A Line?
5
Least Squares Fit
6
RANSAC: Random Sampling and Consensus
7
Line Fitting: Segmentation
8
Line Fitting: Hough Transform
9
Hough Transform Algorithm for Fitting Straight Lines
10
Image Gradient
11
Hough Transform for Polar Form of Equation of Line
12
Line Fitting Examples
13
Noise Factor
14
More Practical Circle Fitting
15
Generalized Hough Transform
16
Generating R-table
17
Detecting shape
18
Rotation and Scale Invariance
Description:
Explore the Hough Transform technique in computer vision through this 48-minute lecture by Dr. Mubarak Shah from the University of Central Florida. Delve into image feature extraction, shape features, and various line-fitting methods including Least Squares Fit and RANSAC. Learn the Hough Transform algorithm for fitting straight lines, understand image gradients, and see practical examples of line and circle fitting. Discover the Generalized Hough Transform, R-table generation, shape detection, and methods for achieving rotation and scale invariance in object recognition.

Hough Transform for Line and Shape Detection - Lecture 18

University of Central Florida
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