Papers
7
Total Citations
270
H-Index
6
About
Amy Tabb is a researcher whose work sits at the intersection of computer vision, robotics, and precision agriculture, with particular focus on automation technologies for orchard and agricultural environments. Her most influential contribution — "Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods" (2017, 115 citations) — has become a foundational reference in robotics calibration, addressing the critical AX = ZB problem that underpins accurate robotic automation. This work builds on her earlier parameterization research (2015), demonstrating a sustained commitment to advancing calibration methodology. In agricultural applications, Tabb pioneered background modeling techniques for real-time apple detection from harvester-mounted video systems (2006, 48 citations) and developed three-dimensional fruit tree reconstruction using shape-from-silhouette methods — essential groundwork for robotic pruning systems. Her 2019 book chapter on agricultural robots in orchard management (46 citations) reflects her broad survey expertise, synthesizing advances across pruning, harvesting, and spraying robotics. More recently, she has contributed to the agricultural robotics community through professional spotlights on the field's growing role in sustainable food production. Tabb's career exemplifies rigorous bridge-building between fundamental computer vision research and real-world agricultural automation challenges.
Research Focus
Key Achievements
Top Papers
- 1Solving the robot-world hand-eye(s) calibration problem with iterative methods115 citations · 2017
- 2Segmentation of Apple Fruit from Video via Background Modeling48 citations · 2006
- 3The use of agricultural robots in orchard management46 citations · 2019
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- 7Agricultural Robotics and Automation [TC Spotlight]6 citations · 2022