光学字符识别中人工感知的例子 虹膜识别

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摘要翻译：
本文假设人类的学习是基于感知的，因此，学习过程和感知不应该被独立地表示和研究，也不应该在不同的仿真空间中建模。为了保持人工学习和人类学习之间的相似性，这里假定前者是基于人工感知的。因此，我们不选择应用或发展（人类）感知的计算理论，而是选择将数字（计算）空间中的人类感知镜像为人工感知，并使用人工智能和软计算中最简单的工具之一，即感知器，在同一数字空间中分析人工学习和人工感知之间的相互依赖。作为实际应用，我们选择了两个例子：光学字符识别和虹膜识别。在这两种情况下，一个简单的图灵测试表明，人工对两个字符和两个虹彩之间差异的感知是模糊的，而相应的人类感知实际上是清晰的。
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英文标题：
《Examples of Artificial Perceptions in Optical Character Recognition and
  Iris Recognition》
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作者：
Cristina M. Noaica, Robert Badea, Iulia M. Motoc, Claudiu G. Ghica,
  Alin C. Rosoiu, Nicolaie Popescu-Bodorin
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.
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PDF链接：
https://arxiv.org/pdf/1209.6195

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关键词：Intelligence Applications Presentation Perceptions Recognition 表示 two 独立 based 学习