Michael Van den Bergh

Papers

Total Citations

195

H-Index

About

Michael Van den Bergh is a leading researcher in computer vision and human-robot interaction, with a focus on real-time 3D gesture recognition and depth sensing. His most influential work, "Real-time 3D hand gesture interaction with a robot for understanding directions from humans" (2011), has garnered 174 citations and pioneered the use of the inexpensive Kinect sensor for robust, real-time hand gesture recognition. By leveraging depth data, Van den Bergh developed a Haarlet-based system that remains effective even in cluttered environments, enabling intuitive human-robot communication. This work has been instrumental in advancing natural user interfaces for robotics and interactive systems. In his subsequent research, "Depth SEEDS: Recovering incomplete depth data using superpixels" (2013), he addressed a critical limitation of IR-based depth sensors—their failure under sunlight. By introducing a superpixel-based method for depth recovery, Van den Bergh expanded the applicability of depth sensing to outdoor and challenging lighting conditions. His contributions have significantly impacted the fields of gesture-based control and robust depth perception, making him a key figure in bridging computer vision and practical robotics.