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

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。这项研究的目标是使高度移动的计算机和手机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，用于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生物合理的引擎的大小和位置无关的图像中的对象识别。 基于事件的传感器和处理硬件的使用有可能增加计算吞吐量和合成vision efficiency.With方面的智力merit，拟议的研究是为了给基于事件的成像设备的能力，从视觉流中提取特定的信息。 在硬件中实现的大小和位置无关的算法是有效的对象识别的一种使能技术，并且是基于事件的人工视觉平台的进步所必需的。 该研究的目标是设计事件驱动的处理硬件，以提取对象的特征与轻量级的数字电路，并利用空间和时间冗余，以增加识别reliability.相对于潜在的更广泛的影响，这项研究有可能让手机作为一个虚拟手杖的盲人，帮助辅助生活的病人，提供环境意识，并扩展人类的感官。 更广泛地说，这项研究可以应用于人工视觉，机器人视觉，图像传感器网络，辅助生活系统和监控系统。 该项目利用该方法的仿生性质来培训学生，特别是妇女和来自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追求研究和学术事业。

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.