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.
Highlights
Hand gesture recognition, utilized in visual input of controlling computers, is one of the most important aspects in human-computer interaction [1]
GMM tractably, tractably, we we introduce introduce two two independent independent k-component k-component GMMs, GMMs, one
To achieve the segmentation automatically, we propose an initial seeds selection method in hand gesture images
Summary
Hand gesture recognition, utilized in visual input of controlling computers, is one of the most important aspects in human-computer interaction [1]. The vision-driven hand gesture recognition method is highly dependent on the sensibility of image sensors, the relatively poor image quality hinders its development. Max-flow/min cut algorithm was applied to minimize the energy function of one and was achieved by this minimized cut.cut These algorithms notnot only focus onon the cut,the andsegmentation the segmentation was achieved by this minimized. These algorithms only focus whole image, and take every morphological detail into account. Gulshan et [13] proposed ansegmentation interactive image segmentation method, shape which as regarded shapecue as for a powerful cue for making object recognition, the problem well posed.
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