[1]DONG Xude,XU Yuanping,SHU Hongping,et al.Static gesture segmentation algorithm model based on centroid watershed algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(2):346-354.[doi:10.11992/tis.201804028]
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Static gesture segmentation algorithm model based on centroid watershed algorithm
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 2
Page number:
346-354
Column:
学术论文—机器学习
Public date:
2019-03-05
- Title:
- Static gesture segmentation algorithm model based on centroid watershed algorithm
- Keywords:
- skin-like background; static gesture segmentation; ICWA algorithm; wrist segmentation; gesture recognition; convexity detection; PCA dimension reduction; deep learning
- CLC:
- TP18
- DOI:
- 10.11992/tis.201804028
- Abstract:
- Considering the difficulty in effectively achieving complete static gesture segmentations from skin-like background regions, this paper proposes an integrated static gesture segmentation model based on an improved centroid watershed algorithm (ICWA). The ICWA algorithm significantly reduces the interference of image gradient on gesture segmentations such that it can completely extract skin regions from images. Moreover, a novel method is designed and implemented by integrating principal component analysis (PCA) dimension reduction and convexity detection algorithms, which can accurately extract the cutting line of the wrist according to convex points. Preliminary experiments of automatic gesture recognitions based on convolutional neural network (CNN) were carried out on a benchmark database. The experimental results show that the proposed model can achieve a recognition rate of 97.85% on average for nine different static gestures.
- References:
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Last Update:
2019-04-25