معرفی
Michael Greenspan is a Professor in the Department of Electrical and Computer Engineering at Queen's University, affiliated with the Ingenuity Labs Research Institute. His expertise spans computer vision, image processing, and robotics. He holds a PhD in Systems and Computer Engineering from Carleton University and has extensive industry experience, including leading the Computational Video Group at the National Research Council of Canada. His research focuses on object recognition, motion planning, and applied computational geometry, with applications in autonomous robotics and 3D scene analysis. He has published over 30 technical papers and 3 patents, and collaborates with industry on applied projects.
Education
- BSc in Physics and Applied Mathematics (1986), University of Toronto
- BASc and MASc in Electrical Engineering (1989, 1991), University of Ottawa
- PhD in Systems and Computer Engineering (1991), Carleton University
Research Interests
- Object recognition and pose determination using geometric probing and minimalist template matching
- Efficient motion planning algorithms for robotic systems
- Computational geometry solutions like TINN for nearest neighbor problems
- Applications in autonomous robotic capture, LiDAR data analysis, and collaborative robotics
Dr. Greenspan’s work bridges theoretical computer vision with practical robotic systems, emphasizing real-time performance and industrial relevance. His lab, the Robotics and Computer Vision Laboratory, develops algorithms for 3D localization, sensor fusion, and autonomous navigation. He is active in professional organizations like IEEE and serves on the Research Management Committee of Precarn Associates.
Key Contributions
- Developed TINN for nearest-neighbor problem optimization
- Pioneered efficient collision detection methods for robotic motion planning
- Advanced 3D point cloud registration techniques using virtual interest points
- Co-created the CycleCrash dataset for bicycle collision analysis
