NDLI: An Interactive Image Segmentation Method in Hand Gesture Recognition

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An Interactive Image Segmentation Method in Hand Gesture Recognition

Content Provider	MDPI
Author	Chen, Disi Li, Gongfa Sun, Ying Kong, Jianyi Jiang, Guozhang Tang, Heng Ju, Zhaojie Yu, Hui Liu, Honghai
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.
File Size	6902784
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s17020253
Journal	Sensors
Issue Number	2
Volume Number	17
Language	English
Publisher Date	2017-01-27
Access Restriction	Open
Subject Keyword	image segmentation Gibbs Energy min-cut/max-flow algorithm sparse representation
Content Type	Text
Resource Type	Article

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