We present an effective and fast method for static hand gesture recognition. This method is based on classifying the different gestures according to geometric-based invariants which are obtained from image data after segmentation; thus, unlike many other recognition methods, this method is not dependent on skin color. Gestures are extracted from each frame of the video, with a static background. The segmentation is done by dynamic extraction of background pixels according to the histogram of each image. Gestures are classified using a weighted K-Nearest Neighbors Algorithm which is combined with a naive Bayes approach to estimate the probability of each gesture type. © 2008 Springer-Verlag.
CITATION STYLE
Ziaie, P., Müller, T., Foster, M. E., & Knoll, A. (2008). A naïve Bayes classifier with distance weighting for hand-gesture recognition. In Communications in Computer and Information Science (Vol. 6 CCIS, pp. 308–315). https://doi.org/10.1007/978-3-540-89985-3_38
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