[lingtalks] Tim Marks Talk, Monday Feb. 25 at 12pm

[Steven Ford]

The UCSD Department of Cognitive Science is pleased to announce a talk by

Tim Marks Ph.D.

Department of Computer Science and Engineering
University of California, San Diego

Monday, February 25, 2008 at 12pm
Cognitive Science Building, room 003


"Probabilistic Models of Visual Processing"

If the brain has developed optimal solutions to the perceptual problems it 
faces, then it may be implementing elegant probabilistic solutions to the 
problems of dealing with an uncertain world. Studying human perception can 
inform new systems for machine perception, and developing new systems for 
machine perception improves our understanding of human and animal 
perception. My research takes advantage of this synergy between human and 
machine perception by developing probabilistic models and deriving optimal 
inference algorithms on those models to enable machines to perform a 
variety of visual processing tasks.

I will first discuss a generative model and probabilistic inference 
algorithm, called G-flow, for tracking a human face (or other deformable 
object) in 3D from single-camera video. Two standard computer vision 
algorithms, optical flow and template matching, emerge as special limiting 
cases of optimal inference under G-flow. This elucidates the conditions 
under which each of these existing methods is optimal and suggests a 
broader family of tracking algorithms that includes an entire continuum 
between these two extremes. Then I will discuss diffusion networks, a 
stochastic version of continuous time, continuous state recurrent neural 
networks. I will present the surprising result that a particular class of 
linear diffusion networks is equivalent to factor analysis, and demonstrate 
that this neurally plausible architecture can be trained, using contrastive 
divergence (Hinton 2002), to learn the type of 3D deformable models used by 
G-flow for face tracking. The same mathematical technique used in G-flow 
can be applied to simultaneous localization and mapping (SLAM) in mobile 
robots. I will present a new algorithm, called Gamma-SLAM, that uses stereo 
vision for SLAM in off-road outdoor environments by representing the world 
using variance grid maps. Next, I will present two probabilistic models of 
visual processing and compare them directly with human performance: NIMBLE, 
a Bayesian model of saccade-based visual memory; and SUN, a Bayesian 
framework for saliency using natural statistics. NIMBLE achieves human 
levels of performance in a face recognition task using a fixation-based 
memory model, and SUN derives a state-of-the-art saliency algorithm from 
the simple assumption that a goal of the visual system is to find potential 
targets that are important to survival. I will conclude with a discussion 
of my plans for future research in each of these areas.
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