Title: Computer Vision - A New Way to Advance Equity in Society
Computer vision is a domain of machine learning that includes methods for processing, analyzing, and extracting high-dimensional data from real world digital images and video. With the use of high-performance computing, image classification, object detection, activity recognition, and tracking can be performed in real-time with accuracies that surpass human-level conclusions. There are numerous highly anticipated breakthrough inventions awaiting society ranging from autonomous vehicles to medical image scans that center on the use of computer vision algorithms. However, the attainment of computer vision represents a more fundamental shift in recognizing human limits in understanding our surroundings and interactions. Specifically, the realization of computer vision is the key to uncover new insights that provide the greatest impact to society, to advance equity among all people and groups. This talk will review the background of computer vision and deep learning techniques in use today, examining unique challenges to fully autonomous edge computing. Then, the talk will examine unique opportunities to use computer vision to uncover and provide value to people.
Dan Connors is an Associate Professor of Electrical and Computer Engineering and Director of the Edge Computing group in the College of Engineering, Design, and Computing (CEDC) at University of Colorado Denver. His research interests span computer vision, machine learning, edge computing, Internet-of-Things (IoT), and parallel computer systems. His group focuses on creating computing systems that accelerate the analysis of visual information. Connors’ research lab has been supported by the National Science Foundation, Department of Energy, Department of Transportation, and NASA. Connors advises AI companies that build edge computer vision systems to solve emerging applications in human behavior analysis, smart city infrastructure, automated agriculture, and industrial processing.