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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaojin Zhu Jie Yang Waibel, A. |
| Copyright Year | 2000 |
| Description | Author affiliation: Interactive Syst. Labs., Carnegie Mellon Univ., Pittsburgh, PA, USA (Xiaojin Zhu) |
| Abstract | Hand segmentation is a prerequisite for many gesture recognition tasks. Color has been widely used for hand segmentation. However, many approaches rely on predefined skin color models. It is very difficult to predefine a color model in a mobile application where the light condition may change dramatically over time. We propose a novel statistical approach to hand segmentation based on Bayes decision theory. The proposed method requires no predefined skin color model. Instead it generates a hand color model and a background color model for a given image, and uses these models to classify each pixel in the image as either a hand pixel or a background pixel. Models are generated using a Gaussian mixture model with the restricted EM algorithm. Our method is capable of segmenting hands of arbitrary color in a complex scene. It performs well even when there is a significant overlap between hand and background colors, or when the user wears gloves. We show that the Bayes decision method is superior to a commonly used method by comparing their upper bound performance. Experimental results demonstrate the feasibility of the proposed method. |
| Starting Page | 446 |
| Ending Page | 453 |
| File Size | 423454 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769505805 |
| DOI | 10.1109/AFGR.2000.840673 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-03-30 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wearable computers Skin Layout Computer displays Application software Fingers Pixel Cameras Image segmentation Decision theory |
| Content Type | Text |
| Resource Type | Article |
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