兴趣点检测是许多计算机视觉应用的基础，如：摄像机定标、三维重建、图像匹配、视频检索、运动估计等。
The detection of interest points is the basis of kinds of computer vision applications, such as: camera calibration, 3d reconstruction, image matching, video retrieval, motion estimation, etc.
序列图像分析的基本任务是从图像序列中检测出运动目标并估计其三维运动参数和结构参数。
It's task is to detect motion object and reconstruct three-dimensional motion and structure parameters from image sequences.
最后，利用稀疏光流进行三维物体运动参数和结构参数的估计。
Finally, we estimate 3-d motion and structure parameters of the body by sparse optical flow.
本文提出了一种多个三维刚体运动的匹配和估计算法，将对应点匹配和运动参数估计结合在一起，转化为代价函数的最优化过程。
This paper presents a matching and estimation algorithm for 3-d rigid body motions, integrating the procedures of the point match and motion estimation into a global optimization process.
本文提出了一种由光流估计刚体的三维运动参数的方法。
In this paper, a method is presented for estimating 3-d motion parameters of a rigid body.
将自适应迭代松弛搜索算法RSA引入到MPEG4模型基编码的三维运动参数估计中 ，解决了估计算法的稳健性问题 。
The problem of robustness in the estimation algorithm is solved by applying relax iterative search algorithm (RSA) to motion estimation of MPEG4 model based coding.
运动估计，包括二维的平面估计和三维的立体估计，是数字视频处理中一个很基本的问题。
Motion estimation, which may refer to image-plane motion (2-D motion) or object-motion (3-D motion) estimation, is one of the fundamental problems in digital video processing.
运动估计，包括二维的平面估计和三维的立体估计，是数字视频处理中一个很基本的问题。
Motion estimation, which may refer to image-plane motion (2-D motion) or object-motion (3-D motion) estimation, is one of the fundamental problems in digital video processing.
应用推荐