Michael Schaeferling
Papers
1
Total Citations
2
H-Index
1
About
Michael Schaeferling’s research lies at the intersection of embedded systems, computer vision, and real-time object recognition, with a particular focus on enabling high-performance perception on low-power hardware. His most cited work, “A Configurable Framework for Hough-Transform-Based Embedded Object Recognition Systems” (2018), addresses a critical challenge in autonomous driving and advanced driver assistance systems (ADAS): achieving reliable object detection under strict energy and computational constraints. Schaeferling’s framework provides a flexible, hardware-aware architecture that leverages the Hough transform for efficient feature extraction, allowing embedded platforms to perform real-time recognition without sacrificing accuracy. This contribution is especially valuable for applications like autonomous vehicles, where latency and power consumption are paramount. With 2 citations, his work has informed subsequent efforts in embedded vision and edge AI. Schaeferling’s achievements demonstrate a deep understanding of the trade-offs between algorithmic complexity and hardware limitations, making him a notable figure in the development of practical, deployable computer vision systems for resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1