8 December 2015 Hand posture recognizer based on separator wavelet networks
Author Affiliations +
Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98750F (2015) https://doi.org/10.1117/12.2228425
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
Abstract
This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tahani Bouchrika, Tahani Bouchrika, Olfa Jemai, Olfa Jemai, Mourad Zaied, Mourad Zaied, Chokri Ben Amar, Chokri Ben Amar, } "Hand posture recognizer based on separator wavelet networks", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750F (8 December 2015); doi: 10.1117/12.2228425; https://doi.org/10.1117/12.2228425
PROCEEDINGS
5 PAGES


SHARE
RELATED CONTENT


Back to Top