Alexandre Bousaid

Papers

Total Citations

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About

Alexandre Bousaid’s research lies at the intersection of computer vision, geometric modeling, and marker-based tracking. His most-cited work, “Introducing a novel marker-based geometry model in monocular vision” (2016), introduces an innovative spherical marker concept that enhances distance measurement accuracy for moving objects from low-resolution monocular images. This contribution addresses a fundamental challenge in vision-based sensing—improving spatial perception with minimal hardware. By leveraging a virtual sphere geometry, Bousaid’s method enables more reliable readings, making it valuable for applications in robotics, autonomous navigation, and motion analysis. Though his citation count (5) reflects a focused, early-stage impact, the work demonstrates a strong conceptual foundation that could influence future marker-based tracking systems. Bousaid’s approach exemplifies how creative geometric modeling can push the boundaries of monocular vision, offering a practical solution for extracting precise 3D information from 2D imagery. His research is particularly relevant for students and engineers seeking efficient, low-cost methods for distance estimation in constrained visual environments.