Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17020253 | SENSORS |
Keywords | Field | DocType |
image segmentation,Gibbs Energy,min-cut/max-flow algorithm,sparse representation | Computer vision,Scale-space segmentation,Computer science,Image texture,Segmentation-based object categorization,Gesture recognition,Image segmentation,Region growing,Random walker algorithm,Artificial intelligence,Minimum spanning tree-based segmentation | Journal |
Volume | Issue | Citations |
17 | 2.0 | 18 |
PageRank | References | Authors |
0.64 | 14 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Disi Chen | 1 | 39 | 7.70 |
| Gongfa Li | 2 | 239 | 43.45 |
| Ying Sun | 3 | 291 | 40.03 |
| Jianyi Kong | 4 | 80 | 13.32 |
| Guozhang Jiang | 5 | 172 | 27.25 |
| Heng Tang | 6 | 30 | 1.49 |
| Zhaojie Ju | 7 | 284 | 48.23 |
| Hui Yu | 8 | 128 | 21.50 |
| Honghai Liu | 9 | 1974 | 178.69 |