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.

人类的视觉系统代表了最复杂的信息处理机，在不同的姿势，照明和尺度下，具有独特的识别对象的功能。神经影像和传感设备的最新进展使这在视力研究中成为令人兴奋的时刻。 现在，我们可以在执行复杂的视觉识别和场景理解时看到大脑“动作”。 EEG，MEG，fMRI和眼睛跟踪等模态已经产生了识别人类视觉处理器使用的基本信息处理策略的神经相关性。计算机视觉研究的最新努力也带来了一些有希望的成功，尽管主要是针对受限制的问题。该研讨会汇集了视觉神经科学，神经计算和计算机愿景领域的世界领导者，以讨论我们对大脑如何迅速识别对象并相对于最先进的计算机视觉系统功能的视觉场景的当前理解。 重点是识别人与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界面。另一个重点是快速场景分析和认可，而不是推理和更高级别的认知。该研讨会的目的是在促进三个单独领域的专家之间促进富有成果的互动方面产生更大的影响。主要结果包括一份报告，该报告详细记录了针对NSF和其他资助机构采取行动的大型研究挑战，机遇和具体建议。