Integrated Computer Vision System Based on Human Eye Motion

基于人眼运动的集成计算机视觉系统

• 批准号: 1662029
• 负责人: Jun Ueda
• 金额: $ 33.27万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662029&HistoricalAwards=false
• 关键词: Integrated; Computer; Vision; System; Based

The goal of this project is to build a robotic vision system that mimics human eye motions. In particular, this research will lead to an enhanced vision system that can achieve quick scanning capability (saccadic eye motion) and smooth object following capability (smooth-pursuit) with minimal computational overhead and using an inexpensive generic hardware. Current state-of-the-art systems require significant computer post-processing and expensive hardware. This project will lead to a novel camera positioning system (hardware) and real-time vision (software) system that does not require any post-processing and produces ready-to-use images in real-time. To achieve this goal, the project draws upon the biomechanical and cognitive principles of human vision. The innovative methods and algorithms that can produce motions with speed, accuracy, and smoothness to mimic human eye can greatly enhance capabilities of autonomous vehicles used in a wide variety of applications such as agriculture, military, security, and traffic monitoring. The project will integrate research and outreach activities for K-12 and undergraduate students. This project studies a bio-inspired dynamics-based method for coordinating motion control and image processing where the system can mechanically displace the field of view by a large angle between frames. The concept behind this research is to merge the system dynamics area and image processing area while previous studies focused solely on either mechanical design or image processing. The method is motivated by the principles of human vision for effective image de-blurring and panoramic image stitching. In coordination with inherently discrete and rapid ocular movements, the developed image processing methods inspired by ocular physiology will mimic saccades and smooth-pursuit in a fast-moving robotic eye. A piezoelectrically driven robotic camera positioning mechanism will be employed to demonstrate the effectiveness of this ocular physiology-inspired approach. Hardware architecture that can synchronize image processing and real-time motion control will be configured and tested. A panoramic image of a scene will be generated from multiple images acquired during saccades by removing motion blur and stitching images within a single frame rate. The system architecture will be optimized to best coordinate hardware and software for real-time motion control.

该项目的目标是构建一个模仿人眼运动的机器人视觉系统。特别是，这项研究将带来一种增强的视觉系统，能够以最小的计算开销并使用廉价的通用硬件实现快速扫描能力（扫视眼运动）和平滑的物体跟随能力（平滑追踪）。 当前最先进的系统需要大量的计算机后处理和昂贵的硬件。 该项目将产生一种新颖的相机定位系统（硬件）和实时视觉（软件）系统，不需要任何后处理并实时生成随时可用的图像。为了实现这一目标，该项目利用了人类视觉的生物力学和认知原理。创新的方法和算法可以产生模仿人眼的速度、准确性和平滑度的运动，可以极大地增强自动驾驶车辆在农业、军事、安全和交通监控等各种应用中的能力。该项目将整合针对 K-12 学生和本科生的研究和外展活动。该项目研究一种基于仿生动力学的方法，用于协调运动控制和图像处理，其中系统可以在帧之间以大角度机械地移动视野。这项研究背后的概念是将系统动力学领域和图像处理领域合并起来，而之前的研究仅关注机械设计或图像处理。 该方法受人类视觉原理的启发，可实现有效的图像去模糊和全景图像拼接。与固有的离散和快速的眼球运动相协调，受眼部生理学启发而开发的图像处理方法将模仿快速移动的机器人眼睛的扫视和平滑追踪。 将采用压电驱动的机器人相机定位机构来证明这种受眼部生理学启发的方法的有效性。将配置和测试能够同步图像处理和实时运动控制的硬件架构。通过消除运动模糊并在单个帧速率内拼接图像，将从扫视期间获取的多个图像生成场景的全景图像。系统架构将进行优化，以最好地协调实时运动控制的硬件和软件。

项目成果

A Direct Drive Parallel Plane Piezoelectric Needle Positioning Robot for MRI Guided Intraspinal Injection

用于MRI引导椎管内注射的直驱平行平面压电针定位机器人

• DOI: 10.1109/tbme.2020.3020926
• 发表时间: 2021
• 期刊: IEEE Transactions on Biomedical Engineering
• 影响因子: 4.6
• 作者: Meinhold, Waiman;Martinez, Daniel Enrique;Oshinski, John;Hu, Ai-Ping;Ueda, Jun
• 通讯作者: Ueda, Jun

Realization of Smooth Pursuit for a Quantized Compliant Camera Positioning System

量化兼容相机定位系统平滑追踪的实现

• DOI: 10.1109/tro.2018.2858272
• 发表时间: 2018
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Kim, Michael D.;Ueda, Jun
• 通讯作者: Ueda, Jun

Camera Trajectory Optimization for Maximizing Optical Character Recognition on Static Scenes with Text,

相机轨迹优化，最大限度地提高静态场景上文本的光学字符识别，

• 发表时间: 2021
• 期刊: and Control Conference (MECC
• 作者: Zabaldo, Alexander;Ueda, Jun
• 通讯作者: Ueda, Jun

Super Resolution for Improved Positioning of an MRI-Guided Spinal Cellular Injection Robot

用于改进 MRI 引导脊髓细胞注射机器人定位的超分辨率

• DOI: 10.1142/s2424905x2140002x
• 发表时间: 2021
• 期刊: Journal of Medical Robotics Research
• 作者: Martinez, Daniel Enrique;Meinhold, Waiman;Oshinski, John;Hu, Ai-Ping;Ueda, Jun
• 通讯作者: Ueda, Jun

Resolution-Enhanced MRI-Guided Navigation of Spinal Cellular Injection Robot

脊髓细胞注射机器人的分辨率增强 MRI 引导导航

• DOI: 10.1109/ismr48331.2020.9312926
• 发表时间: 2020
• 期刊: 2020 International Symposium on Medical Robotics (ISMR
• 作者: Martinez, Daniel Enrique;Meinhold, Waiman;Oshinski, John;Hu, Ai-Ping;Ueda, Jun
• 通讯作者: Ueda, Jun