In this paper, we present a wavelet moment invariants for classification a small change in rotation and subtle difference of hand posture causes misclassifying to other postures. The method combined zernike moment to capture global features and wavelet moment to differentiate between subtle variations in description can be utilized at the same time. Then, a fuzzy classification algorithm is used to classify hand posture. The classification rate obtained is 72% with of Thai sign language.
['Y. Sribooruang', 'Pinit Kumhom', 'Kosin Chamnongthai']
Hand posture classification using wavelet moment invariant