Visualization algorithms

Friday, June 26, 2015 - 1:15pm - 2:15pm
Rayadurgam Srikant (University of Illinois at Urbana-Champaign)
We consider a switch with uniform traffic operating under the MaxWeight scheduling algorithm. This traffic pattern is interesting to study in the heavy-traffic regime since the queue lengths exhibit a multi-dimensional state-space collapse. We use a Lyapunov-type drift technique to characterize the heavy-traffic behavior of the expectation of the sum queue lengths in steady-state. Specifically, in the case of Bernoulli arrivals, we show that the heavy-traffic scaled queue length is n−3/2+1/2n.
Monday, March 6, 2006 - 9:20am - 10:20am
Jitendra Malik (University of California, Berkeley)
Visual grouping and figure-ground discrimination were first studied by
the Gestalt school of visual perception nearly a century ago. By the use
of cleverly constructed examples, they were able to demonstrate the role
of factors such as proximity, similarity, curvilinear continuity and
common fate in visual grouping and factors such as convexity, size, and
symmetry in figure-ground discrimination. However, this left open (at
least) three major problems
(1) there wasn't a precise operationalization
Tuesday, March 7, 2006 - 10:30am - 11:30am
Daniel Kersten (University of Minnesota, Twin Cities)
The traditional model of primary visual cortex (V1) is in terms of a
retinotopically organized set of spatio-temporal filters. This
model has been extraordinarily fruitful, providing explanations of a
considerable body of psychophysical and neurophysiological results.
It has also produced compelling linkages between natural image
statistics, efficient coding theory, and neural responses.
However,there is increasing evidence that V1 is doing a whole lot more. We
Tuesday, March 7, 2006 - 9:00am - 10:00am
Pietro Perona (California Institute of Technology)
How many categories can you recognize? Currently the best
estimate is due to Irv Biederman: 3000 entry-level categories and
perhaps 3*104 categories overall. This estimate was obtained
indirectly, by counting words in a dictionary. I will present a method
to obtain a direct estimate. Alongside the estimate one gets
frequencies of objects and categories for free. I will discuss the
implications for visual recognition and other visual problems.
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