Daniel Tretter

Papers

Total Citations

H-Index

About

Daniel Tretter is a researcher focused on computer vision and human-computer interaction, with a particular emphasis on hand segmentation and hand-object interaction analysis. His most-cited work, "Hand Segmentation for Hand-Object Interaction from Depth map" (2016, 12 citations), addresses a critical preprocessing challenge in augmented reality, medical applications, and human-robot interaction. Tretter’s key contribution lies in developing robust segmentation methods that overcome the limitations of traditional color-based approaches, which fail when objects share skin-like hues or when skin pigmentation varies. By leveraging depth map data, his work enables more reliable hand tracking in complex, real-world environments—a fundamental step for immersive AR experiences and intuitive robotic interfaces. While his citation count reflects a focused, emerging impact, Tretter’s research addresses a persistent bottleneck in interactive systems, offering practical solutions that advance the reliability of gesture recognition and object manipulation technologies. His work is particularly valuable for students and researchers seeking to understand the intersection of depth sensing and human-centered computing, where robust preprocessing remains a key enabler for downstream applications in mixed reality and collaborative robotics.