@inproceedings{eec7ac83377a4000ac4029048b9fe015,
title = "A na{\"i}ve Bayes classifier with distance weighting for hand-gesture recognition",
abstract = "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.",
keywords = "Classification, Gesture Recognition, Human-robot interaction, Image Processing, K-Nearest Neighbors, Na{\"i}ve Bayes",
author = "Pujan Ziaie and Thomas M{\"u}ller and Foster, {Mary Ellen} and Alois Knoll",
year = "2008",
doi = "10.1007/978-3-540-89985-3_38",
language = "English",
isbn = "3540899847",
series = "Communications in Computer and Information Science",
pages = "308--315",
booktitle = "Advances in Computer Science and Engineering - 13th International CSI Computer Conference, CSICC 2008, Revised Selected Papers",
note = "13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008 ; Conference date: 09-03-2008 Through 11-03-2008",
}