Computer Vision - Udacity
Computer Vision - Udacity
Lession 1: Introduction
Lession 2: Images as functions
1. For Black and White
2. For Color
3. Example Code
4. Class
Sample and Quantize
Image Size
Crop
Color planes
Add two images
Multiply by a scalar
Common Types of Noise
Generate Gaussian Noise
Effect of Sigma on Gauusian Noise
Displaying Images in Matlab
Lesson 3: Filtering
Moving Average in 2D
Correlation filtering
Averaging Filter
Gaussian Filter
Variance or Standard Deviation
Keeping the Two Gaussians Straight
Lesson 4: Linearity and convolution
Intro
Impulse Function and Response
Correlation vs Convolution
Property of Convolution
Computational Complexity and Separability
Boundary Issues
Practicing With Linear Filters
Median Filter
Lesson 5: Filters as templates
Template Matching
Lesson 6: Edge detection: Gradients
Edges
Edge Detection
Derivatives and Edges
What is a Gradient
Finite Differences
Partial Derivatives of an Image
The Discrete Gradient
Sobel Operator
Well Known Gradients
But in the Real World
Lesson 7: Edge detection: 2D operators
Derivative of Gaussian Filter 2D
Canny Edge Operator
Canny Edge Detector
Single 2D Edge Detection Filter
Edge Demo
Lesson 8: hough transform: Lines
Parametric Model
Line Fitting
Voting
Hough Space
Polar Representation for Lines
Baic Hough Transform Algorithm
Complexity of The Hough Transform
Hough Example
Hough Demo
Extensions
Lesson 9: Hough transform: Circles
Detecting Circles with Hough
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Hough Space
Hough Space
Let each edge point in image space vote for a set of possible parameters in Hough space
Accumulate votes in discrete set of bins; parameters with the most votes indicate line in image space
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