首页> 外文期刊>Machine Vision and Applications >Layer-based video registration
【24h】

Layer-based video registration

机译:基于层的视频注册

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Registration of a mission video sequence with a reference image without any metadata ( camera location, viewing angles, and reference DEMs) is still a challenging problem. This paper presents a layer-based approach to registering a video sequence to a reference image of a 3D scene containing multiple layers. First, the robust layers from a mission video sequence are extracted and a layer mosaic is generated for each layer, where the relative transformation parameters between consecutive frames are estimated. Then, we formulate the image-registration problem as a region-partitioning problem, where the overlapping regions between two images are partitioned into supporting and nonsupporting (or outlier) regions, and the corresponding motion parameters are also determined for the supporting regions. In this approach, we first estimate a set of sparse, robust correspondences between the first frame and reference image. Starting from corresponding seed patches, the aligned areas are expanded to the complete overlapping areas for each layer using a graph-cut algorithm with level set, where the first frame is registered to the reference image. Then, using the transformation parameters estimated from the mosaic, we initially align the remaining frames in the video to the reference image. Finally, using the same partitioning framework, the registration is further refined by adjusting the aligned areas and removing outliers. Several examples are demonstrated in the experiments to show that our approach is effective and robust.
机译:任务视频序列与没有任何元数据(摄像机位置,视角和参考DEM)的参考图像的配准仍然是一个难题。本文提出了一种基于层的方法,将视频序列注册到包含多个层的3D场景的参考图像中。首先,从任务视频序列中提取鲁棒层,并为每个层生成层镶嵌图,其中估计连续帧之间的相对变换参数。然后,我们将图像配准问题公式化为区域划分问题,其中将两个图像之间的重叠区域划分为支撑区域和非支撑区域(或离群区域),并为支撑区域确定相应的运动参数。在这种方法中,我们首先估计第一帧和参考图像之间的一组稀疏,鲁棒的对应关系。从对应的种子补丁开始,使用设置了级别的图割算法将对齐区域扩展为每一层的完全重叠区域,其中将第一帧注册到参考图像。然后,使用从镶嵌估计的变换参数,我们首先将视频中的其余帧与参考图像对齐。最后,使用相同的分区框架,可通过调整对齐区域并消除异常值来进一步完善配准。实验中证明了几个例子,表明我们的方法是有效且可靠的。

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