Anthony Yezzi
Papers
6
Total Citations
66
H-Index
3
About
Anthony Yezzi is a versatile researcher whose work spans computer vision, robotics, and signal processing, with particular expertise in image reconstruction, shape optimization, and multi-robot coordination. Among his most notable contributions is his pioneering work on event-based image reconstruction, where he formulated the recovery of brightness from bio-inspired event cameras as a linear inverse problem enhanced by deep regularization and optical flow — a technique with significant implications for high-dynamic-range and high-speed robotic vision systems, garnering nearly 30 citations. His research on coverage control algorithms for heterogeneous robot teams, which intelligently accounts for varying maximum speeds to optimize area coverage in time-sensitive scenarios, has attracted 25 citations and reflects his broader interest in practical robotic deployment. Yezzi has also explored radar-based shape reconstruction using variational methods, biologically motivated foraging front optimization inspired by animal predator behavior, and optical flow integration in visual motion tracking. His interdisciplinary approach — bridging mathematical modeling, biological inspiration, and applied robotics — makes his work broadly relevant to researchers in autonomous systems, computer vision, and remote sensing. Students entering these fields will find his contributions foundational to understanding geometric and variational approaches in modern sensing and multi-agent systems.
Research Focus
Key Achievements
Top Papers
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- 5Biologically motivated shape optimization of foraging fronts3 citations · 2011
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