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Sensors 2017, 17(2), 253; doi:10.3390/s17020253

An Interactive Image Segmentation Method in Hand Gesture Recognition

1
School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
2
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
*
Author to whom correspondence should be addressed.
Received: 28 October 2016 / Accepted: 17 January 2017 / Published: 27 January 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy. View Full-Text
Keywords: image segmentation; Gibbs Energy; min-cut/max-flow algorithm; sparse representation image segmentation; Gibbs Energy; min-cut/max-flow algorithm; sparse representation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, D.; Li, G.; Sun, Y.; Kong, J.; Jiang, G.; Tang, H.; Ju, Z.; Yu, H.; Liu, H. An Interactive Image Segmentation Method in Hand Gesture Recognition. Sensors 2017, 17, 253.

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