| 摘要: |
| 根据手势手语的特点,提出了手语语言学和人体运动学相结合的非特定人手语数据的生成和检测方法.
首先,Mean-Shift 算法有控制生成强度的优点,将改进的Mean-Shift 算法应用于手形数据通道的生成,以保持手势手
语的语言学特性,并应用关键手形的音韵标记进行数据有效性的检测;其次,为了丰富手语手势动作的运动特性,将
改进的遗传算法应用于与运动相关的数据通道进行数据生成,并应用拉班舞谱对其进行数据有效性检测;最后,提出
了基于原始样本的检测实验框架,使得所提出的检测方法适用于语言类的多类别数据检测问题.实验结果表明,所提
出的非特定人手语数据的生成和检测方法是有效的. |
| 关键词: 手语识别 音韵标记 拉班舞谱 手语语言学 人体运动学 Mean-Shift 算法 遗传算法 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60603023, 60533030 (国家自然科学基金); theBeijing Municipal Natural Science Foundation of China under Grant No.4061001 (北京市自然科学基金) |
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| Data Generation and Its Validity Inspection of Signer-Independent Sign Language |
|
NI Xun-Bo,ZHAO De-Bin,GAO Wen,JIANG Feng,YAO Hong-Xun
|
| Abstract: |
| This paper proposes the combination of sign language linguistics with human kinematics to generate
and detect the data of SISL (signer independent sign language) according to the characteristics of gesture sign
language (GSL). An improved Mean-Shift algorithm is applied to the generation of hand shape data channels
without losing the linguistic features of GSL, and then the key hand shape phonetic notation is used to detect the
effectiveness of data. In order to enrich the kinematic characteristics of GSL, an improved genetic algorithm is
applied to the generation of movement related data channels. Moreover, Labannotation is adopted to inspect the
effectiveness of data. Finally, an experimental inspection framework is established based on an original sample to
make the proposed detection method adapt to multi-classes data inspection of linguistics. Experimental results show
that the proposed method for the generation and detection of SISL data is effective and feasible. |
| Key words: sign language recognition (SLR) phonetic notation Labannotation sign language linguistics (SLL) human kinematics Mean-Shift algorithm genetic algorithm |
