Fuat Karakaya
Papers
1
Total Citations
3
H-Index
1
About
Fuat Karakaya is a researcher specializing in real-time computer vision and embedded systems, with a particular focus on hardware acceleration for object detection. His work bridges the gap between algorithmic efficiency and practical deployment, notably through the development of a scale- and rotation-invariant object detection algorithm implemented on Field-Programmable Gate Arrays (FPGAs). In his 2016 study, which has garnered 3 citations, Karakaya introduced a computationally light method that combines Histogram of Oriented Gradients (HOG) with the Average Magnitude Difference Function (AMDF) as a decision module to measure shape similarity. This approach enables robust detection in real-time applications, addressing critical challenges in resource-constrained environments. By optimizing hardware implementation for speed and accuracy, Karakaya’s contributions support advancements in autonomous systems, surveillance, and robotics. His work exemplifies the synergy between algorithm design and hardware engineering, offering practical solutions for high-performance, low-latency vision tasks.
Research Focus
Key Achievements
Top Papers
- 1