Structured Robust PCA and Dynamics-based Invariants for Video Understanding: Professor Octavia Camps, Northeastern
- Starts: 1:00 pm on Monday, March 4, 2013
- Ends: 2:00 pm on Monday, March 4, 2013
Bio:
Octavia Camps received a B.S. degree in computer science and a B.S. degree in
electrical engineering from the Universidad de la Republica (Uruguay),
and a M.S.
and a Ph.D. degree in electrical engineering from the University of Washington.
Prof. Camps is a visiting researcher at the Computer Science Department at
Boston University during Spring 2013. Since 2006, she is a Professor in the
Electrical and Computer Engineering Department at Northeastern University. From
1991 to 2006 she was a faculty of Electrical Engineering and of Computer Science
and Engineering at The Pennsylvania State University. In 2000, she was
a visiting faculty at the
California Institute of Technology and at the University of Southern
California. Her main research
interests include robust computer vision, image processing, and
machine learning. She is a former
associate editor of Pattern Recognition and Machine Vision
Applications. She is a member of the
IEEE society.
Talk title:
Structured Robust PCA and Dynamics-based Invariants for Video Understanding
Abstract:
The power of geometric invariants to provide solutions to computer
vision problems has been
recognized for a long time. On the other hand, dynamics-based
invariants remain largely
untapped. Yet, visual data come in streams: videos are temporal
sequences of frames, images are
ordered sequences of rows of pixels and contours are chained sequences
of edges. In this talk, I
will show how making this ordering explicit allows to exploit
dynamics-based invariants to capture
useful information from video and image data. In particular, I will
describe how to efficiently estimate
dynamics-based invariants from incomplete and corrupted data by
formulating the problem as a
structured robust PCA problem, where a structured matrix built from
the data is decomposed into
structured low rank and sparse matrices. Finally, I will show how to
use these invariants to perform
data association, segmentation and classification in the context of
computer vision applications
including dimensionality reduction, tracking and cross-view activity
recognition.
- Location:
- MCS 148