摘要翻译：
我们生活在信息时代，信息已经成为我们生活中至关重要的组成部分。互联网的成功使大量的信息很容易获得，每个人都可以访问。为了保持这种信息的可管理性，迫切需要对其进行无缺陷的循环和有效的处理。今天，大量的研究努力致力于解决这一必要性，但由于缺乏关于“什么是信息？”的共同协议，这些研究工作受到了严重阻碍。尤其是什么是“视觉信息”--人类从周围世界输入的主要信息。从生物视觉研究中借来的一种长期立场进一步加剧了这个问题，这种立场认为类似人类的信息处理是知觉和认知视觉功能的神秘混合。我正试图为这种奇怪的情况找到一种补救办法。基于柯尔莫哥洛夫的紧致性理论和柴丁的算法信息概念，本文提出了一个统一的视觉信息处理框架，它明确地解释了图像处理的知觉特性和认知特性。我相信，这个框架将有助于克服阻碍我们开发类人智能图像处理正确模型的困难。
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英文标题：
《Modeling Visual Information Processing in Brain: A Computer Vision Point
  of View and Approach》
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作者：
Emanuel Diamant
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最新提交年份：
2007
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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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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.
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PDF链接：
https://arxiv.org/pdf/0708.0927