Robust object detection using context and component information

使用上下文和组件信息进行稳健的对象检测

• 批准号: 350703-2007
• 负责人: Sheng, Yunlong
• 金额: $ 1.89万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Department of National Defence / NSERC Research Partnership
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=402161
• 关键词: Robust; object; detection; using; context

Aerial/space remote sensors acquire overwhelming amount of data at the present time for opto-electronic surveillance with multi-spectrum, multi-temporal, georeference data and significantly increased spatial, spectra! and radiometric resolutions. High-resolution aerial images permit to extract 3D information on the grounded objects. Manually processing those data for image registration and fusion, environmental change identification and object detection could be time-consuming and inconsistent for the large-scale image bank. The similar problematic exists in the civil digital libraries, which contain huge amount of visual data in the multimedia and database, thanks to rapid growth in the multimedia acquisition, storage, transmission, visualization, and interaction technologies.

航空/空间遥感器获得压倒性的数据量，在目前的光电监视与多光谱，多时相，地理参考数据和显着增加的空间，光谱！和辐射分辨率。高分辨率航空图像允许提取关于地面物体的3D信息。人工处理这些数据进行图像配准和融合，环境变化识别和目标检测可能是耗时和不一致的大规模图像库。随着多媒体获取、存储、传输、可视化和交互技术的飞速发展，在多媒体和数据库中存储了大量可视化数据的民用数字图书馆也存在着类似的问题。