Udacity

Udacity

Introduction to Computer Vision

Required Course Readings

The occasional text for this course is “Computer Vision: A Modern Approach (2nd edition)” by Forsythe and Ponce. I say occasional because the lectures (and the slides that drive them) are a pretty good set of source material. Occasionally you may need to look at the text to help you decipher the occasional unintelligible lecture.

Post Course Study

• A github repo of resource set: computer-vision-tutorial

• COS429 Fall 2014: Computer Vision

Syllabus

A brief outline of units is given below, grouped into 10 parts:

1 Introduction [Lecture Note]
- 1A Introduction

2 Image Processing for Computer Vision [Lecure Note]
- 2A Linear image processing
- 2B Model fitting
- 2C Frequency domain analysis

3 Camera Models and Views [Lecture Note]
- 3A Camera models
- 3B Stereo geometry
- 3C Camera calibration
- 3D Multiple views

4 Image Features
- 4A Feature detection
- 4B Feature descriptors
- 4C Model fitting

5 Lighting
- 5A Photometry
- 5B Lightness
- 5C Shape from shading

6 Image Motion
- 6A Overview
- 6B Optical flow

7 Tracking
- 7A Introduction to tracking
- 7B Parametric models
- 7C Non-parametric models
- 7D Tracking considerations

8 Classification and Recognition
- 8A Introduction to recognition
- 8B Classification: Generative models
- 8C Classification: Discriminative models
- 8D Action recognition

9 Useful Methods
- 9A Color spaces and segmentation
- 9B Binary morphology
- 9C 3D perception

10 Human Visual System
- 10A The retina
- 10B Vision in the brain

Tracking

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