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Automatic sign language analysis: a survey and the future beyond lexical meaning

机译:自动手语分析:超越词汇意义的调查和未来

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Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures, as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not sufficient for a full understanding of sign language communication. Nonmanual signals and grammatical processes, which result in systematic variations in sign appearance, are integral aspects of this communication but have received comparatively little attention in the literature. In this survey, we examine data acquisition, feature extraction and classification methods employed for the analysis of sign language gestures. These are discussed with respect to issues such as modeling transitions between signs in continuous signing, modeling inflectional processes, signer independence, and adaptation. We further examine works that attempt to analyze nonmanual signals and discuss issues related to integrating these with (hand) sign gestures. We also discuss the overall progress toward a true test of sign recognition systems -dealing with natural signing by native signers. We suggest some future directions for this research and also point to contributions it can make to other fields of research. Web-based supplemental materials (appendices), which contain several illustrative examples and videos of signing, can be found at www.computer.org/publications/dlib.
机译:对手语进行自动分析的研究主要集中在识别手势在连续签名中出现的词汇(或引用)形式,并开发出可很好地适应大型词汇的算法。但是,对词汇符号的成功识别不足以充分理解手势语言交流。非手动信号和语法过程,导致符号外观的系统变化,是这种交流不可或缺的方面,但是在文献中却很少受到关注。在这项调查中,我们研究了用于手语手势分析的数据获取,特征提取和分类方法。关于诸如在连续签名中的符号之间的转换建模,对拐点过程进行建模,签名者独立性和适应性等问题进行了讨论。我们将进一步研究试图分析非手动信号的作品,并讨论与将这些信号与(手势)手势整合在一起的问题。我们还将讨论朝着真正的符号识别系统测试的总体进展-处理本地签名者的自然签名。我们为这项研究提出了一些未来的方向,并指出了它可以对其他研究领域做出的贡献。可在www.computer.org/publications/dlib上找到基于Web的补充材料(附录),其中包含一些说明性示例和视频签名。

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