Murat Peker
Papers
2
Total Citations
6
H-Index
2
About
Murat Peker is a researcher specializing in computer vision, embedded systems, and real-time object detection, with a particular focus on hardware acceleration for autonomous robotics. His work bridges the gap between algorithmic efficiency and practical deployment on resource-constrained platforms like FPGAs. In his 2021 study on stereo disparity, Peker introduced a novel approach using recurrent neural networks to refine matching costs, enabling more accurate depth inference from stereo image pairs—a critical capability for autonomous navigation. Though early in its citation impact, this work addresses a fundamental challenge in robotic perception. Earlier, in 2016, Peker developed a hardware implementation of a scale- and rotation-invariant object detection algorithm on FPGA, combining Histogram of Oriented Gradients (HOG) with the Average Magnitude Difference Function (AMDF) for efficient shape matching. This contribution is notable for its computational lightness and real-time performance, making it suitable for embedded vision systems. With both papers accumulating three citations each, Peker’s research demonstrates a commitment to advancing practical, deployable solutions in computer vision and hardware-software co-design.
Research Focus
Key Achievements
Top Papers
- 1
- 2