A Lightweight Event-Based Synthetic Vision System for Assisted-Living and Machine Vision Applications

用于辅助生活和机器视觉应用的轻量级基于事件的合成视觉系统

• 批准号: 0901742
• 负责人: Andreas Savvides
• 金额: $ 33万
• 依托单位: Yale 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: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901742&HistoricalAwards=false
• 关键词: Lightweight; Event; Based; Synthetic; Vision

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to enable highly mobile computers and cellular phones with the image interpretation capabilities of large bench-top computers. The approach combines new event-based hardware and energy-efficient algorithms for the identification of objects in an image stream. The algorithmic core of this research is a lightweight implementation of a biologically-plausible engine for size and position independent object recognition in images. The use of event-based sensors and processing hardware has the potential to increase computational throughput and synthetic vision efficiency.With respect to intellectual merit, the proposed research is intended to give event-based imaging devices the ability to extract specific information from visual streams. The size- and position-independent algorithms implemented in hardware are an enabling technology for efficient object recognition, and are necessary for the advancement of event-based artificial vision platforms. The research targets the design of event-driven processing hardware to extract object features with lightweight digital circuitry and utilizes spatial and temporal redundancy to increase the identification reliability.With respect to potential broader impacts, this research has the potential to allow a cellular phone to serve as a virtual cane for the blind, help assisted-living patients, provide environmental awareness, and extend human senses. More broadly, this research can be applied to artificial vision, robot vision, image sensor networks, assisted-living systems, and monitoring systems. The project leverages the biomimetic nature of the approach to train students, particularly women and students from other underrepresented groups and to motivate them to pursue careers in research and academia through seminars, classes, and projects.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这项研究的目的是使高度移动的计算机和移动电话具有大型台式计算机的图像解释能力。该方法结合了新的基于事件的硬件和节能算法来识别图像流中的对象。本研究的算法核心是一个轻量级的实现，用于图像中与大小和位置无关的物体识别。使用基于事件的传感器和处理硬件有可能提高计算吞吐量和合成视觉效率。就智力价值而言，所提出的研究旨在赋予基于事件的成像设备从视觉流中提取特定信息的能力。在硬件上实现的尺寸和位置无关的算法是实现高效目标识别的使能技术，也是基于事件的人工视觉平台发展的必要条件。研究目标是设计事件驱动处理硬件，利用轻量级数字电路提取目标特征，并利用空间和时间冗余来提高识别可靠性。就潜在的更广泛的影响而言，这项研究有可能使手机成为盲人的虚拟拐杖，帮助辅助生活患者，提供环境意识，并扩展人类的感官。更广泛地说，本研究可以应用于人工视觉、机器人视觉、图像传感器网络、辅助生活系统和监测系统。该项目利用该方法的仿生特性来培训学生，特别是女性和其他代表性不足群体的学生，并通过研讨会、课程和项目激励他们从事研究和学术界的职业。