Campuses:

Image analysis

Monday, August 10, 2015 - 11:10am - 11:30am
Jesse Berwald (Target Corporation)
Wednesday, August 5, 2015 - 11:20am - 11:40am
Jesse Berwald (Target Corporation), Janet Keel (Target Corporation)
Friday, June 19, 2009 - 9:00am - 10:30am
Gunnar Carlsson (Stanford University)
No Abstract
Tuesday, October 6, 2009 - 3:00pm - 3:30pm
Andrea Bertozzi (University of California, Los Angeles)
Keywords: geometry, image processing, diffuse interface, sparse representations, pan
sharpening

Abstract:
I will present a survey of recent results on geometry-based image
processing. The topics will include wavelet-based diffuse interface
methods, pan sharpening and hyperspectral sharpening, and sparse image
representation.
Tuesday, February 7, 2006 - 1:30pm - 2:30pm
Richard Szeliski (Microsoft Research)
Image-based rendering has been one of the hottest areas in computer
graphics in recent years. Instead of using CAD and painting tools to
construct graphics models by hand, IBR uses real-world imagery to
rapidly create extremely photorealistic shape and appearance
models. However, IBR results to date have mostly been restricted to
static objects and scenes.

Video-based rendering brings the same kind of realism to computer
animation, using video instead of still images as the source
Tuesday, February 7, 2006 - 9:00am - 10:00am
Andrew Fitzgibbon (Microsoft Research)
I shall talk about building 3D models from image sequences, and in
particular about rendering new views of existing sequences in order
to create stereoscopic 3D from monocular footage. I shall show how
existing strategies for image-based rendering can be augmented using
image-based priors to create realistic 3D views. In addition I will
talk about the difficult problem of creating 3D when there is no
camera motion.
Tuesday, January 10, 2006 - 11:00am - 11:45am
Peter Kuchment (Texas A & M University)
Joint with Gaik Ambartsoumian.

In thermoacoustic tomography TAT (sometimes called TCT), one triggers
an ultrasound signal from the medium by radiating it with a short EM
pulse. Mathematically speaking, under ideal conditions, the imaging
problem boils down to inversion of a spherical Radon transform. The talk
will survey known results and open problems in this area.
Monday, January 9, 2006 - 2:30pm - 3:00pm
Justin Romberg (California Institute of Technology)
Many imaging techniques acquire information about an underlying image
by making indirect linear measurements. For example, in computed
tomography we observe line integrals through the image, while in MRI
we observe samples of the image's Fourier transform.
To acquire an N-pixel image, we will in general need to make at least
N measurements.


What happens if the number of measurements is (much) less than N
(that is, the measurements are incomplete)? We will present

Wednesday, December 7, 2005 - 1:30pm - 2:30pm
Rafael Piestun (University of Colorado)
Processing information at the sensor level can not only help reduce the
amount of data acquired but also enhance the overall performance of the
system. In this talk I will first present methods for shaping the
three-dimensional response of an optical system. Then I will discuss how
to integrate tailored optical responses with digital postprocessing
algorithms to improve specific imaging tasks.
Wednesday, December 7, 2005 - 10:30am - 11:30am
James Fienup (University of Rochester)
One can trade off complexity in imaging system hardware and strict
system tolerances for complexity in post-detection data processing.
We describe an extreme example in this trade-off space: a coherent
imaging approach that eliminates the imaging system hardware entirely
(except for the detector array), relying on a phase retrieval
algorithm to form the image in a computer. It has applications
ranging from long-range imaging, such as ballistic missile defense,
to microscopy.

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