Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN
收稿日期: 2018-06-15
网络出版日期: 2018-10-26
Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN
Received date: 2018-06-15
Online published: 2018-10-26
XU Zhengze, ZHANG Wenjun . Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN[J]. 上海大学学报(自然科学版), 2018 , 24(5) : 675 -685 . DOI: 10.12066/j.issn.1007-2861.2073
Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality (VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogram-based threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network (CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest (RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy (88.34% mean intersection over union, mIoU) and a shorter processing time ($\le $8 ms).
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