Christian Scheglmann
Papers
1
Total Citations
2
H-Index
1
About
Christian Scheglmann’s research centers on embedded computer vision and real-time object recognition, with a particular focus on optimizing performance for low-power, resource-constrained devices. His most-cited work, “A Configurable Framework for Hough-Transform-Based Embedded Object Recognition Systems” (2018), addresses the critical challenge of enabling reliable object detection in applications like advanced driver assistance systems (ADAS) and autonomous driving. By developing a flexible, configurable framework that leverages the Hough transform, Scheglmann provides a practical solution for balancing accuracy, speed, and energy efficiency—key trade-offs in embedded deployment. Though his citation count is modest, his contributions are notable for their direct applicability to real-world automotive and robotics systems, where robust performance on limited hardware is paramount. Scheglmann’s work exemplifies the engineering-driven approach needed to bridge the gap between theoretical computer vision and practical, deployable systems. For students and researchers exploring embedded AI or real-time perception, his framework offers a valuable blueprint for designing efficient, customizable recognition pipelines that meet the stringent demands of modern autonomous platforms.
Research Focus
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Top Papers
- 1