Shared Feature Object Detection

这项研究的目标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非常有用。将开发程序，使计算机能够检测到的不同物体的数量急剧增加，并大大扩大计算机能够检测到这些物体的杂波和不利光照条件的范围。需要探索和发展的关键进展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。这种特征共享将显著提高检测多目标的速度和效率，并提高泛化性能。将开发一套标记图像数据库，使研究人员能够训练物体识别算法。所开发的技术可能在诸如机器人、制造和监视等不同领域有用。

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.