Skip to content Skip to navigation

Connexions

You are here: Home » Content » Introduction to Optical Flow

Navigation

Content Actions
  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the authors

Lenses

What is a lens?

Definition of a lens

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects

    This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection:"ELEC 301 Projects Fall 2006"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

Recently Viewed

This feature requires Javascript to be enabled.

Introduction to Optical Flow

Module by: J. Ryan Stinnett, Jennifer Gillenwater, Elica Skorcheva

Summary: A short introduction to optical flow, with a slant towards how it pertains to the projective flow image registration algorithm we used.

Much of the image registration algorithm we chose to use centers on the idea of projective flow between images. Here we will look at the somewhat simpler concept of optical flow, which has been used in computer vision and other applications for decades.

Let's say you only have a one dimensional image, A(x), where A is a function of pixel intensity for each possible location x. You could then translate all the pixels in that image by a value Δx to generate the image B(x). Another way to view this is to think of both images as part of one function, but with an additional parameter t, for time. This extension comes naturally because a difference from the image captured in A to that in B implies that time has elapsed between capturing each of them as well. This gives us the following equations for E(x,t).

E(x,t)=A(x) E(x,t)=A(x) (1)
E(x+Δx,t+Δt)=B(x) E(x+Δx,t+Δt)=B(x) (2)

Extending this into two dimensions is not too hard to imagine, as we would simply have an additional parameter, y. From this information, we can calculate derivatives at each point to determine the optical flow between the two images. One way to view these derivatives is to plot a vector field using the x and y derivatives. Vector fields give a decent picture of the motion from one image to the next, an example of which is included below.

Figure 1: Optical flow field from a taxi driving down a street. (Source: http://www.kfunigraz.ac.at/imawww/invcon/pictures/flowfield.gif)
Sample Optical Flow Field
Sample Optical Flow Field (flowfield.gif)

With this in mind, we can now take a look at how the image registration algorithm we used builds upon the concept introduced here.

Comments, questions, feedback, criticisms?

Send feedback