Informatik-Kolloquien
363. Informatik-Kolloquium
Öffentliche Verteidigung im Rahmen des Promotionsverfahrens
Herr M.Sc. Seyed Armin Dadgar
TU Chemnitz
Fakultät für Informatik
Professur Graphische Datenverarbeitung und Visualisierung
"Application-Independent Recognition of 3D Hand Gestures using Synthetic Data"
Freitag, 16.01.2026, 14:00 Uhr, Straße der Nationen 62, Böttcher-Bau, A12.336 (alt: 1/336)
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Abstract
This dissertation introduces several novel approaches for an overarching context of vision-based application-independent 3D hand gesture recognition using a single RGB image as input. The focus is to leverage synthetic data and to address key challenges in hand detection, segmentation, pose estimation, and dynamic gesture modeling. It proposes a fully hierarchical, layered-based, and flexible hand data structure with a comprehensive one-dimensional PoseDescriptor grounded in finger kinematics to efficiently represent and formulate diverse postures, reducing high degrees of freedom while enabling classification of diverse hand postures and expensive searches (e.g., exhaustive search for 3D pose estimation). It introduces a comprehensive Markovian system that incorporates evolutionary topological constraints to enrich the model of temporal relationships between postures and to enhance the real-time performance and accuracy of the 3D Pose estimation of fingers and hand movement across varied gestures. Finally, it proposes training methodologies for deep neural networks using simplistic synthetic image generation, exploiting the invariancy concept, and mitigating premature saturation to achieve robust results in real-world scenarios with convolutional networks like YOLO for detection and Mask-RCNN for segmentation. The results of this dissertation represent a selection of the successful research undertaken during the PhD period and also include several ideas that light promising directions for future work in this domain.
