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.
Overview
Syllabus
Intro
Image Feature Extraction
Shape Features
How to Fit A Line?
Least Squares Fit
RANSAC: Random Sampling and Consensus
Line Fitting: Segmentation
Line Fitting: Hough Transform
Hough Transform Algorithm for Fitting Straight Lines
Image Gradient
Hough Transform for Polar Form of Equation of Line
Line Fitting Examples
Noise Factor
More Practical Circle Fitting
Generalized Hough Transform
Generating R-table
Detecting shape
Rotation and Scale Invariance
Taught by
UCF CRCV
