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首页> 外文会议>IAPR TC3 International 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：自动手标识识别：通过潜在认知来识别不寻常

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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：手语是听证残疾社区中的主要沟通渠道。自动手语转录可以促进听证残疾社区和听证会之间的更好的沟通和理解。作为最近的自动手语转录的工作已经讨论过，有效处理或识别非签名姿势是关键问题之一。非签名姿势是签名读数的姿势，不属于任何有效的符号。在符号过渡期间或简单地从未意识到姿势可能会出现非标姿势。已经提出了信心率（CR）来减轻问题。如果没有额外的培训，CR易于计算和随时可用。然而，据报道，CR仅部分地解决了这个问题。此外，Cr配方易于计算不稳定。本文提出了对CR的替代制剂，调查泰国手指拼写认可的非签署识别问题，探讨潜在的解决方案并找到了有希望的方向。该发现不仅解决了非签署识别问题，它还在学习良好的推理机器背后提供了一个洞察力，揭示了对潜在机制的隐藏意义和新的解释。我们提出的方法被评估并显示为不符号检测有效。

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