Alexander Zoellner
Papers
1
Total Citations
2
H-Index
1
About
Alexander Zoellner is a researcher focused on embedded computer vision and real-time object recognition, with a particular emphasis on low-power, resource-constrained systems. His work addresses the critical challenge of achieving high-performance visual perception in applications such as advanced driver assistance systems (ADAS) and autonomous driving, where power efficiency and speed are paramount. His most cited paper, "A Configurable Framework for Hough-Transform-Based Embedded Object Recognition Systems" (2018), introduces a flexible architecture that leverages the Hough transform for efficient object detection on embedded platforms. This framework enables developers to tailor recognition pipelines to specific hardware constraints, balancing accuracy and computational cost. While his citation count is currently modest, Zoellner’s contributions are foundational for the growing field of edge AI, where real-time decision-making must occur directly on devices without cloud reliance. His work is particularly relevant for engineers and researchers developing autonomous vehicles, drones, and smart cameras, offering a practical path toward deploying sophisticated vision algorithms in the real world.
Research Focus
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Top Papers
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