• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Regression-based surface reconstruction: Coping with noise, outliers, and discontinuities.
【24h】

Regression-based surface reconstruction: Coping with noise, outliers, and discontinuities.

机译：基于回归的曲面重建：应对噪声，离群值和不连续性。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The automated processing of range data is an important problem in many computer vision applications. Range sensors are being used to aid navigation, object recognition, inspection, and reverse engineering. These applications require range sensors be placed on the factory floor and on autonomous vehicles, creating scenarios well removed from controlled laboratory settings. Instead of recording measurements from a single isolated object with consistent surface properties, range sensors are now exposed to scenes composed of many objects, varying surface properties, and objects occupying different portions of the depth of field and field of view. Not only is the composition of a range scene more complicated but the sensing environment is less amenable to precise and accurate measurements.; While a direct and dense measurement of depth is attractive to many applications, a range map by itself has little practical use. The data is corrupted by noise, randomly of setting each measurement from its true position. The amount of noise can vary across the scene as a function of surface properties, depth, orientation, and position in the field of view. Furthermore, the data contains outliers or completely erroneous measurements in regions of specularity and along discontinuities. Finally, the data is not segmented, so the mapping of measurements to particular object surfaces is not immediate. These problems become more and more difficult to address as scene complexity increases.; Here, we study the statistical properties of range data and surface estimates, constructing statistical tools for simultaneously segmenting and reconstructing complicated range images. First, using order statistics, we construct a robust local surface estimator, called M scUSE, which tolerates a large percentage of outlying data, small scale discontinuities, and multiple surfaces in an image region. Second, using multivariate regression and prediction intervals, we devise statistical decision criteria to control a surface growing segmentation process. These criteria reduce the number of tuning parameters while increasing the sensitivity to small scale discontinuities. We analyze the expected performance of these techniques on synthetic data and include a segmentation comparison study on data from a laser range sensor.
机译：在许多计算机视觉应用中，范围数据的自动处理是一个重要的问题。范围传感器用于辅助导航，物体识别，检查和逆向工程。这些应用要求将距离传感器放置在工厂车间和自动驾驶汽车上，从而将场景从受控实验室设置中完全移除。现在，距离传感器不再是从具有一致表面特性的单个孤立对象中记录测量值，而是将其暴露于由许多对象，变化的表面特性以及占据景深和视场不同部分的对象组成的场景中。不仅场景场景的构成更加复杂，而且传感环境也不适合进行精确的测量。虽然直接而密集的深度测量对于许多应用程序很有吸引力，但是距离图本身几乎没有实际用途。数据被噪声破坏，随机地从其真实位置设置每个测量值。整个场景中的噪声量可能会根据表面属性，深度，方向和视场中的位置而变化。此外，数据包含镜面反射区域和不连续区域中的异常值或完全错误的测量值。最后，数据没有被分割，因此测量到特定对象表面的映射不是立即的。随着场景复杂性的增加，这些问题变得越来越难以解决。在这里，我们研究距离数据和表面估计的统计属性，构建用于同时分割和重建复杂距离图像的统计工具。首先，使用顺序统计数据，我们构造了一个健壮的局部表面估计器，称为M scUSE，它可以容忍较大比例的外围数据，小规模的不连续点以及图像区域中的多个表面。其次，使用多元回归和预测区间，我们设计统计决策标准来控制表面生长分割过程。这些标准减少了调整参数的数量，同时增加了对小规模不连续点的灵敏度。我们分析了这些技术对合成数据的预期性能，并包括对激光测距传感器数据的分段比较研究。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号