Unsupervised Learning for Stereo Vision

author: David McAllester, Toyota Technological Institute at Chicago
published: July 30, 2009,   recorded: June 2009,   views: 6127


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


We consider the problem of learning to estimate depth from stereo image pairs. This can be formulated as unsupervised learning - the training pairs are not labeled with depth. We have formulated an algorithm which maximizes conditional likelihood the left image given right image in a model that involves latent information (depth). This unsupervised learning algorithm implicitly trains shape from texture and shape from shading monocular depth cues. The talk will present pragmatic results in the stereo vision problem as well as a general formulation of models and methods for maximizing conditional likelihood in a latent variable model where we wish to interpret the latent information as "labels".

See Also:

Download slides icon Download slides: mlss09us_mcallester_ulsv_01.pdf (977.1┬áKB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: