BioCOMP: Biologically Inspired Computational Model for Perception

BioCOMP：受生物启发的感知计算模型

• 批准号: 0727129
• 负责人: Bir Bhanu
• 金额: $ 24万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727129&HistoricalAwards=false
• 关键词: BioCOMP; Biologically; Inspired; Computational; Model

Abstract: The University of California Riverside is awarded a grant to develop biologically inspired computational models for visual perception. Perceptual systems take raw sensory input like digital photographs/movies and identify the objects they contain. Decades have been spent trying to develop perceptual systems, with only modest success. The key innovation of this research is that it incorporates the biological constraints neuroscientists have identified that guide the developmental of natural perceptual systems into the automated development of computational perceptual systems. The project involves an interdisciplinary team and a close collaboration between a computer scientist and a cognitive psychologist. The systems developed in this research learn to work in a way that is similar to biological systems. The project develops a new paradigm for incorporating domain-specific knowledge in this case, biological constraints into evolutionary computation to develop innovative visual systems. This approach systematically addresses the complexity and magnitude of the object detection/recognition problem in real-world environments. The research generates computational innovations to permit the development of evolutionary learning systems that can utilize developmental neuropsychological constraints. These include (a) cooperative coevolution that allows components of a task to evolve in an environment in which cooperation improves fitness, (b) smart crossover and mutation operators that retain effective components over generations of computational evolution, and (c) a minimum description length constraint that selects operators based on efficiency of description in addition to goodness of fit. The goal of these innovations, collectively and individually, is to reduce the volume of the search space that the evolutionary learning process must traverse to allow it to solve the perceptual problem. The project will use several publicly available databases to demonstrate the results.

摘要：加州河滨大学获得了一项赠款，以开发以生物学启发的视觉感知模型。感知系统采用原始感官输入，例如数字照片/电影，并识别它们所包含的对象。 几十年来，尝试开发感知系统，只有适度的成功。 这项研究的关键创新是，它结合了神经科学家确定的生物学约束，这些生物学约束指导自然感知系统的发展中，以自动化计算感知系统的自动发展。 该项目涉及一个跨学科团队以及计算机科学家与认知心理学家之间的密切合作。这项研究中开发的系统学会以类似于生物系统的方式工作。 该项目开发了一种新的范式，用于在这种情况下将特定于领域的知识纳入特定于领域的知识，将生物学约束纳入进化计算中，以开发创新的视觉系统。这种方法系统地解决了实际环境中对象检测/识别问题的复杂性和幅度。该研究产生了计算创新，以允许开发可以利用发育神经心理学约束的进化学习系统。其中包括（a）合作协调，使任务的组成部分可以在合作改善健身的环境中发展，（b）智能跨界和突变操作员，这些智能跨界和突变操作员保留了几代计算进化的有效组件，（c）最小描述长度约束，该长度约束基于效率的效率，除了效率的效率。这些创新的目的集体，单独地是为了减少进化学习过程必须穿越以解决感知问题的搜索空间的数量。该项目将使用几个公开可用的数据库来证明结果。