Lesson 1

# Introduction to Computer Vision

Course

This course provides an introduction to computer vision including fundamentals, methods for application and machine learning classification.

This course provides an introduction to computer vision including fundamentals, methods for application and machine learning classification.

Last Updated March 4, 2022

## Course Lessons

Lesson 2

#### 2A-L1 Images as functions

Lesson 3

#### 2A-L2 Filtering

Lesson 4

#### 2A-L3 Linearity and convolution

Lesson 5

#### 2A-L4 Filters as templates

Lesson 6

#### 2A-L5 Edge detection: Gradients

Lesson 7

#### 2A-L6 Edge detection: 2D operators

Lesson 8

#### 2B-L1 Hough transform: Lines

Lesson 9

#### 2B-L2 Hough transform: Circles

Lesson 10

#### 2B-L3 Generalized Hough transform

Lesson 11

#### 2C-L1 Fourier transform

Lesson 12

#### 2C-L2 Convolution in frequency domain

Lesson 13

#### 2C-L3 Aliasing

Lesson 14

#### 3A-L1 Cameras and images

Lesson 15

#### 3A-L2 Perspective imaging

Lesson 16

#### 3B-L1 Stereo geometry

Lesson 17

#### 3B-L2 Epipolar geometry

Lesson 18

#### 3B-L3 Stereo correspondence

Lesson 19

#### 3C-L1 Extrinsic camera parameters

Lesson 20

#### 3C-L2 Instrinsic camera parameters

Lesson 21

#### 3C-L3 Calibrating cameras

Lesson 22

#### 3D-L1 Image to image projections

Lesson 23

#### 3D-L2 Homographies and mosaics

Lesson 24

#### 3D-L3 Projective geometry

Lesson 25

#### 3D-L4 Essential matrix

Lesson 26

#### 3D-L5 Fundamental matrix

Lesson 27

#### 4A-L1 Introduction to "features"

Lesson 28

#### 4A-L2 Finding corners

Lesson 29

#### 4A-L3 Scale invariance

Lesson 30

#### 4B-L1 SIFT descriptor

Lesson 31

#### 4B-L2 Matching feature points (a little)

Lesson 32

#### 4C-L1 Robust error functions

Lesson 33

#### 4C-L2 RANSAC

Lesson 34

#### 5A-L1 Photometry

Lesson 35

#### 5B-L1 Lightness

Lesson 36

#### 5C-L1 Shape from shading

Lesson 37

#### 6A-L1 Introduction to motion

Lesson 38

#### 6B-L1 Dense flow: Brightness constraint

Lesson 39

#### 6B-L2 Dense flow: Lucas and Kanade

Lesson 40

#### 6B-L3 Hierarchical LK

Lesson 41

#### 6B-L4 Motion models

Lesson 42

#### 7A-L1 Introduction to tracking

Lesson 43

#### 7B-L1 Tracking as inference

Lesson 44

#### 7B-L2 The Kalman filter

Lesson 45

#### 7C-L1 Bayes filters

Lesson 46

#### 7C-L2 Particle filters

Lesson 47

#### 7C-L3 Particle filters for localization

Lesson 48

#### 7C-L4 Particle filters for real

Lesson 49

#### 7D-L1 Tracking considerations

Lesson 50

#### 8A-L1 Introduction to recognition

Lesson 51

#### 8B-L1 Classification: Generative models

Lesson 52

#### 8B-L2 Principle Component Analysis

Lesson 53

#### 8B-L3 Appearance-based tracking

Lesson 54

#### 8C-L1 Discriminative classifiers

Lesson 55

#### 8C-L2 Boosting and face detection

Lesson 56

#### 8C-L3 Support Vector Machines

Lesson 57

#### 8C-L4 Bag of visual words

Lesson 58

#### 8D-L1 Introduction to video analysis

Lesson 59

#### 8D-L2 Activity recognition

Lesson 60

#### 8D-L3 Hidden Markov Models

Lesson 61

#### 9A-L1 Color spaces

Lesson 62

#### 9A-L2 Segmentation

Lesson 63

#### 9A-L3 Mean shift segmentation

Lesson 64

#### 9A-L4 Segmentation by graph partitioning

Lesson 65

#### 9B-L1 Binary morphology

Lesson 66

#### 9C-L1 3D perception

Lesson 67

#### 10A-L1 The retina

Lesson 68

#### 10B-L1 Vision in the brain

Lesson 69

#### We're Done!

Lesson 70

#### Sandbox

## Taught By The Best

### Aaron Bobick

Instructor

### Irfan Essa

Instructor

### Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

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