[Colloquium] Kevin Murphy Talk Mon. March 8 at TTI

[Meridel Trimble]

Toyota Technological Institute Talk

Speaker:  Kevin Murphy, MIT
Speaker's Homepage:  www.ai.mit.edu/~murphyk

Date:  Monday, March 8, 2004
Time:  2:30 PM
Place:  TTI-C, 1427 E. 60th St., 2nd Floor (University Press Building)

Refreshments Provided

Title:  "Probabilistic graphical models for scene and object recognition"

Abstract: 
Probabilistic graphical models are a way of combining multiple sources of noisy
evidence together in a principled fashion, in order to come up with an optimal
estimate of the hidden state of a system. Well-known examples include Kalman
filters and HMMs. In this talk, I will show how we can use graphical models to
perform fast and robust place and scene recognition. I will then show how to
extend the model to detect objects such as cars, people, computers, etc. We use
the output of the scene recognition system to decide which objects are likely to
be present (for example, cars are unlikely in indoor scenes).  Next we use
global image features to predict the likely location of the object. Finally we
apply a standard object detector (based on boosted decision stumps) to the
image. The various sources of information are combined using a discriminatively
trained graphical model (a conditional random field).  We discuss some methods
for efficiently training such models, and demonstrate our system on a
challenging dataset of indoor and outdoor images collected with a wearable camera.

If you have questions, or would like to meet the speaker, please contact Carole
at 773.702.5033 or cfkipp at tti-c.org.

For information on future TTI-C talks or events, please go to the TTI-C Events
page at http://www.tti-c.org/events.shtml
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