CSE 559A: Computer Vision


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Fall 2018: T-R: 11:30-1pm @ Lopata 101

Instructor: Ayan Chakrabarti (ayan@wustl.edu).
Course Staff: Zhihao Xia, Charlie Wu, Han Liu

http://www.cse.wustl.edu/~ayan/courses/cse559a/

Aug 27, 2018

Introduction

 
 

What is Computer Vision ?

Endow machines with the ability to make sense

of the physical world by looking at

images and videos

Introduction

 
 

What is Computer Vision ?

Endow machines with the ability to make sense

of the physical world by looking at

measurements of reflected light

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Introduction

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

Why is this hard ?

So how does it work ?


By making assumptions about and exploiting structure in the natural world

So how does it work ?

So how does it work ?

So how does it work ?

So how does it work ?

So how does it work ?

So how does it work ?

Illusions

Illusions

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

History of CV

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

Syllabus Overview

  • Image formation, representation, and processing
  • Low level vision
    • Photometric
    • Geometric
    • Motion
  • Segmentation & Grouping
  • High Level Vision (reasons with "semantic" knowledge)
    • Using deep convolutional neural networks


Slides will be posted on course website after class

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/


Prerequisites

  • Programming: problem sets will be in Python.
  • Math: Linear Algebra, Vector Calculus, Probability and Statistics.
  • Also helpful: Prior coursework on machine learning, signal processing, graphics.

Please fill out survey on course website (also sent to you by e-mail) ASAP

This Course

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/


Prerequisites

  • Programming: problem sets will be in Python.
  • Math: Linear Algebra, Vector Calculus, Probability and Statistics.
  • Also helpful: Prior coursework on machine learning, signal processing, graphics.

Please fill out survey on course website (also sent to you by e-mail) ASAP

Also, go through resources section for refreshers for math, python background and optional reference books.

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

  • 5 Problem Sets
    • Math (answers to be typeset in LaTeX) and Programming (in Python)
    • 15% x 5 = 75% of your grade
    • To be done individually
    • Roughly every two weeks (see website)
    • READ collaboration and late policy
    • Submitted using git.

TODO
- Read Problem Set section
- Install Anaconda
- Set up git and submit your public key

This Course

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

  • 5 Problem Sets
    • Math (answers to be typeset in LaTeX) and Programming (in Python)
    • 15% x 5 = 75% of your grade
    • To be done individually
    • Roughly every two weeks (see website)
    • READ collaboration and late policy
    • Submitted using git.

TODO
- Read Problem Set section
- Install Anaconda
- Set up git and submit your public key

Problem Set 0 is a trivial ungraded problem set to test your setup (LaTeX, Anaconda, Git).

git clone cse559@euclid.seas.wustl.edu:wustl.key/pset0

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

  • 5 Problem Sets
    • Math (answers to be typeset in LaTeX) and Programming (in Python)
    • 15% x 5 = 75% of your grade
    • To be done individually
    • Roughly every two weeks (see website)
    • READ collaboration and late policy
    • Submitted using git.
  • Final Project
    • To be done individually
    • Open ended, mini research/implementation project
    • Choose topic (suggestions on course website), submit brief proposal, and get feedback from us
    • End of Term: Report = 25% of your grade

No Exams

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

  • Extra Credit: Class Participation = 5%
  • Sign up for Piazza !
    • Ask questions on piazza: more efficient than e-mail, can also be anonymous
    • Answer others' questions (subject to collaboration policy): counts towards credit


  • Office Hours
    • Prof. Chakrabarti: Wed 9:30am-10:30am, Jolley Hall @ 205
    • Zhihao: Fri 10:30-Noon @ Jolley Hall 309
    • Charlie & Han: Mon 5:30pm-6:30pm @ TBD
    • Do come and ask questions sooner rather than later
    • No participation credit for office hours

This Course

All information @ http://www.cse.wustl.edu/~ayan/courses/cse559a/

  • Read through course website
    • Syllabus
    • Late Policies
    • Collaboration & Academic Honesty Policies
  • Join Piazza
  • Review Pre-reqs and Fill out survey
  • Submit your Public Key
  • Submit Problem Set 0

Questions ?

The Pinhole Camera

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The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera

The Pinhole Camera