Speaker: Jianbo Shi, University of Pennsylvania
Location: Warren Weaver Hall 1302
Date: April 29, 2011, 11:30 a.m.
Host: Chris Bregler
We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative.
We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. Instead of modeling 2D spatial transformation of pixels, we compute attribute transformation on image features, such as edge orientation or histogram of oriented image gradient, in a higher dimensional space. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples.
Jianbo Shi studied Computer Science and Mathematics as an undergraduate at Cornell University where he received his B.A. in 1994. He received his Ph.D. degree in Computer Science from University of California at Berkeley in 1998. He joined The Robotics Institute at Carnegie Mellon University in 1999 as a research faculty, where he lead the Human Identification at Distance(HumanID) project, developing vision techniques for human identification and activity inference. In January 2003, he joined the Department of Computer & Information Science at University of Pennsylvania where he is currently an Associate Professor, and Graduate Group Chair coordinating the Ph.D. and Professional Master education across six programs in Computer Science. He received a National Science Foundation CAREER award in 2005, and IEEE Longuet-Higgins Prize, a contribution that has stood the test of time for his work on “Normalized Cuts and Image Segmentation” in 2007.
His current research focus on human behavior analysis and image recognition-segmentation. His other research interests include image/video retrieval, and vision based desktop computing. His long-term interests center around a broader area of machine intelligence, he wishes to develop a "visual thinking" module that allows computers not only to understand the environment around us, but also to achieve higher level cognitive abilities such as machine memory and learning.
Refreshments will be offered starting 15 minutes prior to the scheduled start of the talk.