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Introduction to Computer Vision


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


No experience required

Course Lessons

Lesson 1

1A-L1 Introduction

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


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


Taught By The Best

Photo of Aaron Bobick

Aaron Bobick


Photo of Irfan Essa

Irfan Essa


Photo of Arpan Chakraborty

Arpan Chakraborty


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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Introduction to Computer Vision


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