Title :
Hand posture classification using wavelet moment invariant
Author :
Sribooruang, Y. ; Kumhom, P. ; Chamnongthai, K.
Author_Institution :
Dept. of Electron. & Telecommun. Eng., King Mongkut´´s Univ. of Technol., Bangkok, Thailand
Abstract :
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.
Keywords :
Zernike polynomials; feature extraction; gesture recognition; image classification; method of moments; wavelet transforms; Thai sign language; feature extraction; fuzzy classification algorithm; hand posture classification; hand rotation; image classification; wavelet moment invariant; zernike moment; Application software; Computer science; Fingers; Handicapped aids; Histograms; Human computer interaction; Principal component analysis; Robots; Shape; Virtual reality;
Conference_Titel :
Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2004. (VECIMS). 2004 IEEE Symposium on
Print_ISBN :
0-7803-8339-7
DOI :
10.1109/VECIMS.2004.1397192
