[talks] Y Liu generals
Melissa M. Lawson
mml at CS.Princeton.EDU
Thu May 10 13:13:35 EDT 2012
Yiming Liu will present his research seminar/general exam on
Thursday May 17 at 10AM in Room 402. The members of his
committee are: Szymon Rusinkiewicz (advisor), Adam Finkelstein,
and Kai Li. Everyone is invited to attend his talk, and those
faculty wishing to remain for the oral exam following are
welcome to do so. His abstract and reading list follow below.
Single-shot Multispectral Imaging
Multispectral imaging is a method to obtain the spectrum of each pixel
in a 2D image. Instead of roughly aggregating light arriving at each
pixel into one channel (e.g. gray-scale images) or three channels (RGB
images), multispectral imaging divides the spectrum of each pixel into
much narrower wavelength bands, obtaining a 3D cube of data indexed by
spatial position and wavelength. Some prior works have shown promising
acquisition quality, but they either require tedious manual work and
very long acquisition time, or need active illumination or even
additional cameras. Our work is a single-shot multispectral imaging
method, which tries to capture a multispectral cube with one camera and
within a single camera shot. A diffraction grating is put between camera
lens and the scene to diffract lights from the scene into one clear
zero-order image, and several dispersed first-order images. Each image
is a projection of the 3D spectral cube on a plane. Based on these
images and projections, and based on a prior assumption that camera
noise is with a Poisson distribution, an maximum likelihood estimation
algorithm is applied to infer the 3D spectral cube. Several synthetic
and real experiments are designed to analyze the sensitivity of the
acquisition quality with respect to calibration error and camera noise.
Szeliski, R. (2010). Computer vision: Algorithms and applications.
Springer-Verlag New York Inc.
1. Cao, X., Du, H., Tong, X., Dai, Q.,& Lin, S. (2011). A Prism-Mask
System for Multispectral Video Acquisition. IEEE transactions on pattern
analysis and machine intelligence, 1-14.
2. Cao, X., Tong, X.,& Dai, Q. (2011). High resolution multispectral
video capture with a hybrid camera system. CVPR.
3. Descour, M.,& Dereniak, E. (1995). Computed-tomography imaging
spectrometer: experimental calibration and reconstruction results.
Applied optics, 34(22), 4817-26.
4. Habel, R., Kudenov, M.,& Wimmer, M. (2012). Practical Spectral
Photography. Eurographics (Vol. 31).
5. Hagen, N.,& Dereniak, E. L. (2008). Analysis of computed tomographic
imaging spectrometers. I. Spatial and spectral resolution. Applied
optics, 47(28), F85-95.
6. Mohan, A., Raskar, R.,& Tumblin, J. (2008). Agile Spectrum Imaging:
Programmable Wavelength Modulation for Cameras and Projectors.
7. Schechner, Y. (2002). Generalized mosaicing: Wide field of view
multispectral imaging. Pattern Analysis and Machine, 24(10), 1334-1348.
8. Brodzik, A. K.,& Mooney, J. M. (1999). Convex projections algorithm
for restoration of limited-angle chromotomographic images. Journal of
the Optical Society of America A, 16(2), 246.
More information about the talks