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低级计算机视觉任务中的自主几何精度误差估计

Autonomous Geometric Precision Error Estimation in Low-level Computer Vision Tasks
课程网址: http://videolectures.net/icml08_paisley_agpee/  
主讲教师: John Paisley
开课单位: 杜克大学
开课时间: 2008-08-29
课程语种: 英语
中文简介:
使用计算机视觉的地图制作任务中的错误很少。我们通过考虑采用立体匹配算法来对现实世界点进行三角测量的数字高程模型的构建来证明这一点。这种稀疏性与作者最近开发的几何误差理论相结合,允许自主代理人独立于地面事实来计算他们自己的精度。我们将这些发展与稀疏信号重建或压缩感知数学的最新进展联系起来。这里提出的理论将20世纪90年代发现的3D模型重建的自主性扩展到它们的误差。
课程简介: Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that employ stereo matching algorithms to triangulate real-world points. This sparsity, coupled with a geometric theory of errors recently developed by the authors, allows for autonomous agents to calculate their own precision independently of ground truth. We connect these developments with recent advances in the mathematics of sparse signal reconstruction or compressed sensing. The theory presented here extends the autonomy of 3-D model reconstructions discovered in the 1990s to their errors.
关 键 词: 计算机视觉; 地图制作; 立体匹配算法
课程来源: 视频讲座网
最后编审: 2019-04-19:lxf
阅读次数: 76