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Using a Naïve Bayes Classifier for Static Hand Gesture Recognition

By on February 22, 2020

Publication Type: Academic
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 in variants 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 naïve Bayes approach to estimate the probability of each gesture type. When this method was tested in the domain of the JAST human-robot dialog system, it classified more than 93% of the gestures correctly into one of three classes.
Publication Year: 2008
Location: Springer Berlin Heidelberg
Type: Journal, Book
Publication Venue: Advances in Computer Science and Engineering
Language: English
Tags: image recognition, naive Bayes, robotics,
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