The University of Rochester's Center for Electronic Imaging Systems (CEIS) has announced a record $114 million economic impact in New York State during its 2005-2006 fiscal year.
Over the past five years, CEIS has delivered $315 million in economic impact in New York State in terms of new jobs, revenues, cost savings, capital improvements, and acquired funds. The center actively reaches out and networks to support workforce development through sponsored showcases, seminars, and technical events.
With 39 projects across a wide range of companies, CEIS reported to the New York State Office of Science, Technology, and Academic Research (NYSTAR) that technology created by its researchers and commercially developed by local industrial partners has delivered record benefits. The report is compiled from data prepared by company officers who annually give CEIS the results of collaborative projects.
"We are especially pleased with this achievement in light of the diversity of technologies transferred to a large number of regional companies," said Eby G. Friedman, CEIS director. "We believe that our progress reflects a deliberate effort to focus our request for proposal process on projects that demonstrate increasingly significant economic impact potential. Our researchers are making excellent progress toward their stated goals and expected return."
CEIS benefits the regional economy by matching researchers at the University of Rochester and other regional institutions with New York State and Rochester region businesses, including smaller companies that might not be able to afford hiring full-time scientists. The businesses benefit by receiving cutting-edge research, while the scientists benefit by having additional funding to carry out their research.
One highlight is the CEIS relationship with PL E-Communications. CEIS worked with the Rochester company from its initial research efforts through the successful launch of its product that addresses the issue of nuisance and false alarms in security systems. Automatic change detection in video software currently used in perimeter security has historically been plagued with numerous false alarms, in many cases resulting in the detection system being turned off.
Chris Brown and Randall Nelson, professor and associate professor of computer science, respectively, performed the analysis and experimentation required to commercialize a University-provided algorithm to automatically recognize an object in motion in a video feed with far more accuracy and far fewer false alarms. The military is now testing the technology to protect fences and borders by recognizing human movement in surveillance camera feeds.