Structured Robust PCA and Dynamics-based Invariants for Video Understanding: Professor Octavia Camps, Northeastern
- 1:00 pm on Monday, March 4, 2013
- 2:00 pm on Monday, March 4, 2013
- MCS 148
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.