Sule Yildirim Yayilgan

About

Sule Yildirim Yayilgan is a leading researcher at the intersection of computer vision, robotics, and intelligent systems, with a particular focus on enhancing safety and precision in both medical and industrial domains. Her most impactful work, "StereoScenNet: surgical stereo robotic scene segmentation" (18 citations), pioneers semantic segmentation for robot-assisted surgery, enabling safer, minimally invasive laparoscopic procedures by teaching machines to understand surgical scenes from video. This contribution is critical for the next generation of computer-assisted surgical systems. Complementing this, her research on "Pixelwise object class segmentation based on synthetic data" (14 citations) and efficient real-time labeling for human-robot collaboration (2 citations) demonstrates her commitment to safe industrial automation, using RGB-D sensors to identify human body parts in shared workspaces. Yayilgan’s work on semi-autonomous mobile robots for teaching path-planning theory further highlights her dedication to bridging advanced research with practical education. With a growing citation footprint and a focus on translating synthetic data into real-world robotic perception, Yayilgan is a key figure in making human-robot interaction both smarter and safer.

Research Focus

Key Achievements

Top Papers

1. 1
   StreoScenNet: surgical stereo robotic scene segmentation

   18 citations · 2019
2. 2
   Pixelwise object class segmentation based on synthetic data using an optimized training strategy

   14 citations · 2014
3. 3
   Intelligent Technologies and Applications

   6 citations · 2022
4. 4
   Control System of a Semi-Autonomous Mobile Robot**The work has been supported by the Funds of University of Pardubice, Czech Republic, grant No. SGS-2016-017.

   3 citations · 2016
5. 5
   Efficient real-time pixelwise object class labeling for safe human-robot collaboration in industrial domain

   2 citations · 2015

Key Collaborators

Contact & Links