technical reconstruction

technical reconstruction的基本解释

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技术改造

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

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。

3D reconstruction is one of the most challenging problems in computer vision. This project uses CAD model to increase the accuracy,robustness and speed of 3D reconstruction and five different works are conducted: 1 research on space curve 3D reconstruction method based on high curvature point nad turning point; 2 free-form curve 3D reconstruction based on B-spline and BURBS; 3 Active surface affine reconstruction based on single frame; 4 Matching and reconstruction of 3D sparse points; Data fitting method based on the iteration of nearest point.

三维重建是计算机视觉中公认的难题，本项目使用CAD模型来提高三维重建的精度、稳健性和速度的思路，围绕着空间曲线和曲面的三维重建和匹配开展了以下五个方面的研究：1）基于高曲率点和拐点分割的空间曲线三维重建方法研究；2）基于B样条和NURBS表示的空间自由曲线三维重建方法研究；3）基于单帧图像的有源曲面仿射重建；4）三维离散数据的匹配与重建；5）基于迭代最近点的数据拟合方法。

The reconstruction problem is ill-posed, so two optimal criterions, the least module and the smoothness criterion base on Tikhonov regularization technique, are introduced into reconstruction algorithm. Many regularization parameters choice strategies are investigated, and the TPA(Two—Parameter Algorithm) strategy which is based on the Morozov discrepancy principles, is implemented in two regularization reconstruction algorithms.Numerical experiment results show that the nonnegative and smoothness constraint condition can overcome the difficulty of iteration semiconvergent, preconditioned technique can improve convergence rate and reconstruction accuracy, smoothness regularization criterion can meliorate ill-posed problem of reconstruction and enhance iteration stability, and the TPA is an effective strategy of regularization parameters choice.

数值试验表明：在共轭梯度法中引入非负约束和光滑约束改善了迭代的"半收敛"性，非负约束保证了解的非负性，光滑约束抑制了重建解的振荡现象，约束算法的重建精度与无约束算法相比大幅度提高；在约束共轭梯度重建算法中引入预优技术，可以加快算法的收敛速度，提高迭代的稳定性和重建精度；引入光滑准则的正则化技术可以有效改善图像重建问题的不适定性，加快迭代的收敛速度，提高迭代的稳定性和图像重建质量，计算正则参数的TPA算法在闪光照相图像重建中是有效的。

与technical reconstruction相关的网络解释 [注：此内容来源于网络，仅供参考]

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