| 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, |
| Reference: | Click To Download |
| Link: | Click To Visit |
