Lecture

Perceptual grouping of natural shapes in cluttered backgrounds using iterative multi-scale tensor voting
25.07.2011
Speaker: Dr. George Bebis, Computer Vision Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, USA
Date: 25 July 2011 Time: 12:00 - 14:00
Location: Stelios Orphanoudakis Seminar Room
Host: Antonis Argyros

Abstract:

Perceptual grouping (or organization) can be defined as the ability to detect organized structures or patterns in the presence of missing and noisy information. It has been shown to be of fundamental importance in computer vision, providing reliable information to higher level functions, such as object detection and recognition. In this talk, I will consider the problem of grouping oriented segments in highly cluttered images and present an iterative, multi-scale tensor voting methodology for segmenting natural shapes in cluttered backgrounds. In this context, segments are represented as second-order tensors and communicate with each other through a voting scheme that incorporates the Gestalt principles of visual perception. The key idea is removing background segments conservatively on an iterative fashion, using multi-scale analysis, and re-voting on the retained segments. We have performed extensive experiments to evaluate the strengths and weaknesses of our approach using both synthetic and real images from publicly available datasets. Our results and comparisons show improved segmentation results, especially under severe background clutter.

Bio:

George Bebis received the B.S. degree in mathematics and M.S. degree in computer science from the University of Crete, Greece in 1987 and 1991, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Central Florida, Orlando, in 1996. Currently, he is a Professor in the Department of Computer Science and Engineering at the University of Nevada, Reno (UNR) and Founder/Director of the Computer Vision Laboratory (CVL). His research interests include computer vision, image processing, pattern recognition, machine learning, and evolutionary computing. His research has been funded by NSF, NASA, ONR, NIJ, Ford Motor Company, Honda, and the Nevada Department of Transportation (NDoT). Dr. Bebis is an associate editor of the Machine Vision and Applications Journal, and serves on the editorial board of the International Journal on Artificial Intelligence Tools. He has served on the program committees of various national and international conferences, and has organized and chaired several conference sessions. In 2002, he received the Lemelson Award for Innovation and Entrepreneurship.

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