TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kopinski, Thomas
A1  - Gepperth, Alexander
A1  - Handmann, Uwe
T1  - A time-of-flight-based hand posture database for human-machine interaction
N2  - We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.
Y1  - 2016
UR  - https://ieeexplore.ieee.org/document/7838613/authors#authors
ER  -