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homography相关的网络例句

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与 homography 相关的网络例句 [注:此内容来源于网络,仅供参考]

We systemically discuss how to uniquely decide an infinite plane homography matrix by using the structure information in scene and how to evaluate a homography matrix which convert affine reconstruction to Euclidean reconstruction by solving absolute conic images.

系统地讨论了如何利用场景中的结构信息,来唯一地确定无穷远平面的单应矩阵,进而由射影重构恢复仿射重构,以及如何通过绝对二次曲线的像求解将仿射重构变换为欧氏重构的单应矩阵。

In the first part, depending on three or more images, the main research work are listed as follows:(1) using SVD decomposition to realize projective reconstruction;(2) realizing camera self-calibration by solving Kruppa's equation;(3) recovering Euclidean reconstruction from projective reconstruction. Depending on only two images, the main researches are:(1) making out infinite plane homography matrix by using scene structure information, then recovering affine reconstruction from projective reconstruction;(2) making out the absolute conic images by using scene structure information, and then recovering Euclidean reconstruction from projective reconstruction.

在第一部分中,针对三幅及三幅以上的图像,主要研究:①利用矩阵奇异值分解实现射影重构,②通过求解Kruppa方程实现摄像机自标定,③由射影重构恢复欧氏重构;针对只有两幅图像的情况,主要研究:①利用场景结构信息求解无穷远平面的单应矩阵,由射影重构恢复仿射重构,②利用场景结构信息求解绝对二次曲线的像,由仿射重构恢复欧氏重构。

Main contents of the thesis include point and edge extraction based on least-squares template matching; space circle template matching and reconstruction; outlier detection and elimination with fundamental matrix and trifocal tensor; improvement of Homography-based calibration technique, 2D-DLT and bundle adjustment based non-metric camera calibration technique with planar-scenes, point-line hybrid photogrammetry and visual inspection.

本文的主要研究内容包括影像格网点与直线段的自动最小二乘模板匹配;基于物方直接解的空间圆匹配与重建;利用基本矩阵和三视张量进行观测值粗差的剔除;同形矩阵摄像机标定算法的改进;基于二维 DLT 和光束法平差的非量测数字摄像机标定;点、线混合摄影测量模型与视觉检测精度分析等。

First, an initial projection matrix is obtained through homography between an image and a scene plane. The homograph matrix between two images, i. e., the image plane and the scene plane, can then be calculated from matching points of these two images. Thus the projection matrix of every next frame can be calculated from homograph matrix between the current image plane and the previous one. Registration of virtual objects is finally completed.

根据这个原理,首先通过一个景物平面和图像之间的单应得到初始状态相机的投影矩阵,然后对后面每帧图像和前一帧进行自然特徵点匹配,通过匹配点对计算出图像之间以及图像与景物平面的单应关系,由此就可以得到每一帧图像对应的相机的投影矩阵,从而完成虚拟物体的注册。

In some sense, our approach can be thought to have decomposed the computation of homography matrix into two simple calibration processes so that the computational load becomes lighter for the calibration of multiple cameras.

就某个方面来看,我们的方法可被视为将homography矩阵的运算,拆解成两个简单的校正过程,因此可以减少多台摄影机校正的运算量。

Homography plays an important role in visual metrology, camera calibration and 3D reconstruction. However, how to robustly and accurately estimate a homography from images is still a difficult problem in practice.

摘要单应矩阵估计在视觉测量、摄像机标定、三维重建等领域有重要的应用价值,但是在具体应用中如何鲁棒、精确地估计单应矩阵仍是一个没有很好解决的问题。

We can then use this set of unwarped perspective images to calculate the relative poses of the calibration plate as well as the projection error of the feature points on the calibration plate by using the homography method.

接下我们就可以用所计算出的这组反扭曲影像,计算出虚拟相机与校正物之间的相对姿势,且再用homography方法我们可以计算出校正版上每个特徵点的校正误差。

We analyze the essence of affine reconstruction and prove the sufficient conditions that a reversible matrix can be an infinite plane homography matrix and we can not uniquely decide an infinite plane homography matrix from fundamental matrix.

详细分析了仿射重构的本质,证明了可逆矩阵为无穷远平面单应矩阵的充分条件,以及从基本矩阵无法唯一确定无穷远平面单应矩阵。

Secondly, in the determination of the homography of the plane at infinity, it relies neither on projective reconstruction nor on the homography of a space plane, it needs only some image correspondences and epipoles, which are basic requirements for any camera self-calibration technique.

该方法主要优点在于:在确定无穷远平面的单应性矩阵的过程中,不需要射影重构,也不需要有限远平面信息,唯一所需要的信息是图象极点,从而简化了文献中现有的算法。

Firstly, the key frames were extracted from the video sequence. Then, the features detected between adjacent key frames were guided and matched by motion displacement, which was calculated by the optical flow field. With the RANSAC robust estimation and cascade of homography matrix, the homography matrix of the adjacent key frame and the nonadjacent key frame was got. Finally, the seamless mosaic was rendered by image blending.

首先在同一镜头下的视频序列中提取若干关键帧,利用关键帧拼接表示序列的拼接;其次利用光流场算法计算出的运动位移量引导相邻关键帧间特征点的匹配,并结合随机抽样一致性鲁棒估计算法和单映矩阵的级联性,得到相邻关键帧、非相邻关键帧间的对应矩阵;最后通过融合实现了序列的无缝拼接。

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