Trainable Visual Aids for Object Detection and Identification

用于物体检测和识别的可训练视觉辅助工具

• 批准号: 0209289
• 负责人: Gert Cauwenberghs
• 金额: $ 86.75万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2005-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0209289&HistoricalAwards=false
• 关键词: Trainable; Visual; Aids; Object; Detection

This project leverages advances in statistical learning theory, machine vision, and massively parallel very-large-scale-integration technology to develop a custom-trainable, versatile, self-contained, and mobile system for visually impaired users. The system will aid the user in interacting freely with other people and the environment, by rapidly detecting and localizing key visual environmental cues and rapidly recognizing and identifying familiar people and objects. At the core of the system is the "Kerneltron", a massively parallel Support Vector "Machine" (SVM) in silicon. The SVM hardware will be trained on-line by the end user to accommodate a variety of visual detection and recognition tasks in everyday situations through presentation of examples. The recognition core will be embedded in a portable prototype visual aid, interfacing with a CCD camera front-end, and an audio synthesizer back-end. Menu-driven keypad control will allow direct input and feedback from the user in training and directing the system. The user interface will be based on "OpenEyes", a wearable computer vision system for the blind. Proof of concept demonstration of the hardware system and evaluation of the training and test performance will be conducted with feedback from volunteer impaired users.

该项目利用统计学习理论、机器视觉和大规模并行超大规模集成技术的进步，为视障用户开发可定制、可训练、多功能、独立的移动系统。 该系统将通过快速检测和定位关键视觉环境线索以及快速识别和识别熟悉的人和物体，帮助用户与其他人和环境自由交互。 该系统的核心是“Kerneltron”，这是一个大规模并行硅支持向量“机”(SVM)。 SVM 硬件将由最终用户进行在线培训，通过示例演示来适应日常情况下的各种视觉检测和识别任务。 识别核心将嵌入便携式原型视觉辅助设备中，与 CCD 摄像头前端和音频合成器后端连接。 菜单驱动的键盘控制将允许用户在培训和指导系统时直接输入和反馈。 用户界面将基于“OpenEyes”，这是一种针对盲人的可穿戴计算机视觉系统。 硬件系统的概念验证演示以及培训和测试性能的评估将根据志愿者受损用户的反馈进行。