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首页> 外文会议>IAPR TC3 workshop on artificial neural networks in pattern recognition >Automatic Hand Sign Recognition: Identify Unusuality Through Latent Cognizance
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Automatic Hand Sign Recognition: Identify Unusuality Through Latent Cognizance

机译：自动手势识别：通过潜在认知识别异常

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摘要

Sign language is a main communication channel among a hearing disability community. Automatic sign language transcription could facilitate better communication and understanding between a hearing disability community and a hearing majority. As a recent work in automatic sign language transcription has discussed, effectively handling or identifying a non-sign posture is one of the key issues. A non-sign posture is a posture unintended for sign reading and does not belong to any valid sign. A non-sign posture may arise during a sign transition or simply from an unaware posture. Confidence ratio (CR) has been proposed to mitigate the issue. CR is simple to compute and readily available without extra training. However, CR is reported to only partially address the problem. In addition, CR formulation is susceptible to computational instability. This article proposes alternative formulations to CR, investigates an issue of non-sign identification for Thai Finger Spelling recognition, explores potential solutions and has found a promising direction. Not only does this finding address the issue of non-sign identification, it also provide an insight behind a well-learned inference machine, revealing hidden meaning and new interpretation of the underlying mechanism. Our proposed methods are evaluated and shown to be effective for non-sign detection.
机译：手语是听力障碍社区之间的主要沟通渠道。自动手语转录可以促进听力障碍社区与大多数听力者之间更好的沟通和理解。正如最近在自动手语转录中的工作所讨论的那样，有效地处理或识别非手语姿势是关键问题之一。非标志姿势是指不打算用于标志读取的姿势，并且不属于任何有效的标志。非符号姿势可能会在符号过渡期间出现，也可能只是从不知道的姿势中出现。已经提出了置信度（CR）来缓解此问题。 CR易于计算，无需额外培训即可轻松获得。但是，据报告CR仅部分解决了该问题。此外，CR公式易受计算不稳定的影响。本文提出了替代CR的方法，研究了用于泰国手指拼写识别的非符号识别问题，探索了潜在的解决方案，并找到了一个有希望的方向。这一发现不仅解决了非符号识别的问题，而且还提供了一个知识渊博的推理机背后的洞察力，揭示了隐藏的含义以及对潜在机制的新解释。我们提出的方法经过评估，证明对非符号检测有效。

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