TY - GEN
T1 - Comparison study of different feature classifiers for hand posture classification
AU - Baek, Jeonghyun
AU - Kim, Jisu
AU - Kim, Euntai
PY - 2013
Y1 - 2013
N2 - Hand posture classification has attracted much attention in Human-Computer Interaction (HCI). In hand posture classification, vision based approach is popularly used. However, it has difficulty of dealing with illumination change and pose variation. In this paper, we compare the performance of combination with features, which are HOG, LBP, and classifiers, which are SVM and Neural Network for hand posture classification. Experiments are performed with Cambridge hand gesture dataset.
AB - Hand posture classification has attracted much attention in Human-Computer Interaction (HCI). In hand posture classification, vision based approach is popularly used. However, it has difficulty of dealing with illumination change and pose variation. In this paper, we compare the performance of combination with features, which are HOG, LBP, and classifiers, which are SVM and Neural Network for hand posture classification. Experiments are performed with Cambridge hand gesture dataset.
UR - http://www.scopus.com/inward/record.url?scp=84893549307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893549307&partnerID=8YFLogxK
U2 - 10.1109/ICCAS.2013.6703956
DO - 10.1109/ICCAS.2013.6703956
M3 - Conference contribution
AN - SCOPUS:84893549307
SN - 9788993215052
T3 - International Conference on Control, Automation and Systems
SP - 683
EP - 687
BT - ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems
T2 - 2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
Y2 - 20 October 2013 through 23 October 2013
ER -