Dr. George Nagy, Rensselaer Polytechnic Institute

"Computer Assisted Visual Interactive Recognition"

Tuesday, March 29, 4:00 PM

Packard Lab -- Room 416

Abstract: The bottleneck in interactive visual classification is the exchange of information between human and machine. Computer Assisted Visual Interactive Recognition (CAVIAR) introduces the concept of the visible model, which is an abstraction of an object
superimposed on its picture.

The parameterized geometrical model in CAVIAR serves as the human-computer communication channel. The iterative recognition process is modeled as a finite state machine. Evaluation of a flower recognition systems on 30 naive subjects shows that 1) the accuracy of the CAVIAR system is much higher than that of the machine alone; 2) its recognition time is much lower than that of the human alone; 3) it can be initialized with as few as one training sample per class and still achieve high accuracy; 4) it demonstrates self-learning. A CAVIAR face-recognition system yields similar results. CAVIAR-flower has been ported to a stand-alone and to a wireless laptop-client PDA.


Bio:  George Nagy received the B.Eng. and M.Eng. degrees from McGill University, and the PhD in Electrical Engineering from Cornell University in 1962 (on neural networks). For
the next ten years he studied pattern recognition at the IBM T.J. Watson Research Center in Yorktown Heights. From 1972 to 1985 he was Professor of Computer Science at the University of Nebraska - Lincoln (nine years as chair), and worked on geographic information systems, remote sensing applications, and human-computer computer interfaces. Since 1985 he has been Professor of Computer Engineering at Rensselaer Polytechnic Institute, where he established ECSE DocLab. In addition to document image analysis, OCR, geographic information systems and computational geometry, his students have engaged in solid modeling, finite-precision spatial computation, and interactive computer vision, often with a focus on systems that improve with use.