公开(公告)号：US09317968B2

公开(公告)日：2016-04-19

申请号：US14059812

申请日：2013-10-22

Applicant: Raytheon Company

Inventor： Stephen J. Raif

IPC: G06T17/00 ,  G06T7/00 ,  G06K9/00

CPC classification number: G06T17/00 ,  G06K9/00637 ,  G06T7/579

Abstract: The system and methods described herein operate on a plurality of images that include multiple views of the same scene, typically from slightly different viewing angles and/or lighting conditions. One of the images is selected as a reference image. For each image ray in a non-reference image, the system and methods resample a local region from the non-reference image's space to the reference image's space. The resampling is performed multiple times, each time with a different surface orientation hypothesis. The system and methods run cross-correlation style correlators on the resampled images, evaluate correlation scores for each of the resampled images, and select the surface orientation hypothesis associated with the highest correlation score. The system and methods project a peak of the correlation surface back through a geometry model for the selected surface orientation hypothesis to determine a three-dimensional (ground) location for the image ray.

Abstract translation: 本文描述的系统和方法在包括相同场景的多个视图的多个图像上操作，通常来自略微不同的视角和/或照明条件。 选择其中一幅图像作为参考图像。 对于非参考图像中的每个图像光线，系统和方法将局部区域从非参考图像的空间重新采样到参考图像的空间。 进行多次重采样，每次采用不同的表面取向假设。 系统和方法在重采样图像上运行互相关样式相关器，评估每个重采样图像的相关性分数，并选择与最高相关分数相关的表面方位假说。 系统和方法通过用于所选择的表面取向假说的几何模型来投射相关表面的峰值，以确定图像光线的三维（地面）位置。

公开(公告)号：US20140253543A1

公开(公告)日：2014-09-11

申请号：US14201008

申请日：2014-03-07

Applicant: Raytheon Company

Inventor： Stephen J. Raif ,  John J. Coogan ,  Steven B. Seida ,  Paul L. Pryor ,  Carlton E. Nance ,  Wyatt D. Sharp, III ,  Jody D. Verret

IPC: G06T17/00

CPC classification number: G06T17/00 ,  G06F17/30241 ,  G06F17/30256 ,  G06T7/0002 ,  G06T17/05 ,  G06T2200/04 ,  G06T2207/10028 ,  G06T2207/30168 ,  G06T2210/56

Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.

Abstract translation: 从被动图像生成点云的系统和方法。 形成图像簇，其中每个图像簇包括从一组无源图像中选择的两个或更多个被动图像。 基于每个图像集群的性能预测分数，为每个图像集群预测可以从每个图像集群生成的点云的质量。 基于其性能预测分数，选择图像簇的子集用于进一步处理。 对于生成的每个点云确定特定任务质量得分，并选择具有最高质量得分的点云进行存储。