无监督的集群和集体学习,用于将唇部关节分类为指形拼写
Nurzada Amangeldy1, Nazerke Gazizova1, Marek Milosz2
1Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010008, Kazakhstan.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种使用无监督集群的新方法,用于分析手指拼写中的唇部运动. 这种方法提取了强大的视觉模式,提高了手语识别的准确性和可解释性.
科学领域:
- 计算机视觉 计算机视觉
- 语言学的语言学.
- 人与计算机的交互
背景情况:
- 由于唇形的变化和模糊性,手语识别面临着挑战.
- 现有的方法经常与原始视觉特征作斗争,限制了准确性.
研究的目的:
- 开发一种可靠的方法来分析手指拼写时的唇部关节.
- 提取可靠的视觉模式,以增强手语识别.
主要方法:
- 无监督的唇部运动轨迹的集群,以确定一致的关模式.
- 探索各种集群配置和评估强度.
- 基于模型的对歧视潜力的集群表示的评估.
主要成果:
- 轨迹集群有效地产生用于手语识别的特征.
- 这种方法提高了提取的唇部特征的解释性和稳定性.
- 证明了唇部动态作为辅助方式的重要性.
结论:
- 无监督的唇部轨迹聚类是特征生成的实用方法.
- 该方法提供了更准确和上下文敏感的手势解释.
- 突出了口唇动态在多式联络手语识别系统中的潜力.
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