Model-Based High-Dimensional Pose Estimation with Application to Hand Tracking

http://nbn-resolving.de/urn:nbn:de:gbv:46-00102865-17
http://elib.suub.uni-bremen.de/peid=D00102865
urn:nbn:de:gbv:46-00102865-17
Mohr, Daniel
2012
Universität Bremen: Informatik/Mathematik
Computer Vision, Object Detection, Object Recognition, Tracking, Hand Pose Estimation
This thesis presents novel techniques for computer vision based full-DOF human hand motion estimation. Our main contributions are:
A robust skin color estimation approach; A novel resolution-independent and memory efficient representation of hand pose silhouettes, which allows us to compute area-based similarity measures in near-constant time; A set of new segmentation-based similarity measures; A new class of similarity measures that work for nearly arbitrary input modalities; A novel edge-based similarity measure that avoids any problematic thresholding or discretizations and can be computed very efficiently in Fourier space; A template hierarchy to minimize the number of similarity computations needed for finding the most likely hand pose observed; And finally, a novel image space search method, which we naturally combine with our hierarchy. Consequently, matching can efficiently be formulated as a simultaneous template tree traversal and function maximization.
DDC
000
ACM
I.4.8
2012.11.30/11:56:30
Model-Based High-Dimensional Pose Estimation with Application to Hand Tracking
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