Shared Feature Object Detection

共享特征对象检测

• 批准号: 0413232
• 负责人: William Freeman
• 金额: $ 34万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-11-15 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413232&HistoricalAwards=false
• 关键词: Shared; Feature; Object; Detection

The goal of this research is to improve the ability of computers to recognize objects in the world. That ability is useful for a range of robotic, surveillance, and image interpretation tasks. Procedures will be developed that should allow for a dramatic scale-up in the numbers of different objects a computer can detect, as well as greatly expanding the range of clutter and adverse lighting conditions under which the computer can detect those objects. The key advance to be explored and developed is the sharing of visual features across different objects, as well as other imaging dimensions of viewpoint, lighting, scale, and position. This feature sharing should dramatically improve the speed and efficiency of detecting multiple objects, as well as improve generalization performance. A set of labeled image databases will be developed that will allow researchers to train object recognition algorithms. The techniques developed may be useful in such diverse areas as robotics, manufacturing, and surveillance.

这项研究的目的是提高计算机识别世界上物体的能力。这种能力对于一系列机器人、监视和图像解释任务都很有用。将制定程序，使计算机可以探测到的不同物体的数量急剧增加，并大大扩大计算机可以探测到这些物体的杂乱和不利照明条件的范围。需要探索和开发的关键进步是在不同对象之间共享视觉特征，以及视点，照明，比例和位置等其他成像维度。这种特征共享应该可以显著提高检测多个对象的速度和效率，并提高泛化性能。将开发一组标记图像数据库，使研究人员能够训练对象识别算法。 开发的技术可能在机器人、制造和监控等不同领域有用。