Systems Seminar
Learning the "Epitome" of an Image
Prof. Brendan Frey
ECE Department
University of Toronto
Abstract
I will describe a new model of image data that we call the "epitome".
The epitome of an image is its miniature, condensed version containing the
essence of the textural and shape properties of the image. As opposed to
previously used simple image models, such as templates or basis functions,
the size of the epitome is considerably smaller than the size of the image
or object it represents, but the epitome still contains most constitutive
elements needed to reconstruct the image. A collection of images often
shares an epitome, e.g., when images are a few consecutive frames from a
video sequence, or when they are photographs of similar objects. A
particular image in a collection is defined by its epitome and a smooth
mapping from the epitome to the image pixels. When the epitome model
is used within a hierarchical generative model, appropriate inference
algorithms can be derived to extract epitomes from a single image
or a collection of images and at the same time perform various inference
tasks, such as image segmentation, motion estimation, object removal,
super-resolution and image denoising.
Go to http://www.research.microsoft.com/~jojic/epitome.htm for a sneak preview.
Joint work with Nebojsa Jojic and Anitha Kannan.
Time and Place: Wed., Feb. 18, at 2:20 pm in 3609 EH
*** NOTE SPECIAL TIME & PLACE ***
SYSTEMS SEMINAR WEB PAGE:
http://www.cae.wisc.edu/~gubner/seminar/