Workshop on Hybrid Neuro-Computer Vision Systems

混合神经计算机视觉系统研讨会

• 批准号: 0958402
• 负责人: Shih-Fu Chang
• 金额: $ 5万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0958402&HistoricalAwards=false
• 关键词: Workshop; Hybrid; Neuro; Computer; Vision

Human visual systems represent the most complex information processing machines, with unique capabilities in recognizing objects at a glance, under varying poses, illuminations and scales. Recent advances in neuroimaging and sensing device makes this an exciting time in vision research. We can now see the brain "in action" while it performs complex visual recognition and scene understanding. Modalities such as EEG, MEG, fMRI, and eye tracking have yielded identifying neural correlates underlying information processing strategies used by the human visual processor. Recent efforts of computer vision research have also yielded some promising successes, though mostly for constrained problems. This workshop brings together world-leaders in the fields of visual neuroscience, neural computing, and computer vision to discuss our current understanding of how the brain is able to rapidly recognize objects and analyze a visual scene relative to the capabilities of state-of-the-art computer vision systems. Emphasis is placed on identifying synergies between human and machine vision and potential neural interfaces that could be used to create hybrid vision systems. An additional focus is on rapid scene analysis and recognition, rather than reasoning and higher level cognition. The workshop aims at broader impacts in facilitating fruitful interaction among experts from three separate fields. The major outcome includes a report that documents in detail the grand research challenges, opportunities, and concrete recommendations of actions for NSF and other funding agencies.

人类视觉系统代表着最复杂的信息处理机器，具有在不同姿势、照明和比例下一目了然地识别对象的独特能力。神经成像和传感设备的最新进展使这一时期成为视觉研究中一个令人兴奋的时代。当大脑执行复杂的视觉识别和场景理解时，我们现在可以看到大脑在“活动”。诸如EEG、MEG、fMRI和眼睛跟踪等模式已经产生了识别人类视觉处理器使用的潜在信息处理策略的神经关联。计算机视觉研究最近的努力也取得了一些有希望的成果，尽管主要是针对有约束的问题。这次研讨会汇集了视觉神经科学、神经计算和计算机视觉领域的世界领先者，讨论我们目前对大脑如何能够快速识别对象并分析视觉场景的理解，相对于最先进的计算机视觉系统的能力。重点是确定人类和机器视觉之间的协同作用，以及可用于创建混合视觉系统的潜在神经接口。另一个重点是快速的场景分析和识别，而不是推理和更高水平的认知。讲习班的目的是在促进来自三个不同领域的专家之间富有成效的互动方面产生更广泛的影响。主要成果包括一份报告，该报告详细记录了NSF和其他资助机构面临的重大研究挑战、机遇和具体的行动建议。