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 projective depth 射影深度(2)射影深度(2)射影深度(2)
 射影深度
 The method estimates projective depth of line measure matrix,does SVD and applies transform to make the result validate. Conjugate iterative to estimate projective depth is applied in the method. 算法首先对直线测量矩阵估计射影深度,采用SVD(Singular Value Decomposition)分解矩阵,并最后应用变换,使得分解结果真实有效。 短句来源 The projective depth will be more exact. 在估计射影深度时运用了共轭法迭代,使得射影深度的估算更准确。 短句来源
 射影深度
 The method estimates projective depth of line measure matrix,does SVD and applies transform to make the result validate. Conjugate iterative to estimate projective depth is applied in the method. 算法首先对直线测量矩阵估计射影深度,采用SVD(Singular Value Decomposition)分解矩阵,并最后应用变换,使得分解结果真实有效。 短句来源 The projective depth will be more exact. 在估计射影深度时运用了共轭法迭代,使得射影深度的估算更准确。 短句来源
 射影深度
 The method estimates projective depth of line measure matrix,does SVD and applies transform to make the result validate. Conjugate iterative to estimate projective depth is applied in the method. 算法首先对直线测量矩阵估计射影深度,采用SVD(Singular Value Decomposition)分解矩阵,并最后应用变换,使得分解结果真实有效。 短句来源 The projective depth will be more exact. 在估计射影深度时运用了共轭法迭代,使得射影深度的估算更准确。 短句来源
 “projective depth”译为未确定词的双语例句
 In addition,it does not need the computation of the projective depth and consequently the fundamental matrix. 它无需估计投影深度,避免了基础矩阵计算的复杂性问题,因而也不受相机特殊运动的限制。 短句来源

我想查看译文中含有：的双语例句

 projective depth
 This section presents a solution to the latter problem based on recovering projective depth.
 In this paper we propose an algorithm of 3D reconstruction based on image sequences,The projective depths are iteratively estimated by using conjugate gradient method. we carry out the projective reconstruction by matrix factorization. Then we transform projective reconstruction into euclidean reconstruction by using a 4x4 non-singular matrix. The result indicates it is efficient. 提出一种基于图象序列的 3D重构算法。采用共轭梯度法迭代估计射影深度 ,通过矩阵分解方法实现射影重构。然后利用一个 4× 4非奇异矩阵 ,将射影重构变换为欧氏重构。实验结果表明此算法是行之有效的 In this paper, we propose an algorithm for hierarchical reconstruction. The affine projection is used to approximate perspective projection; the projective depths are iteratively estimated by linear iterative method. And then, the projective reconstruction is obtained by SVD of the measurement matrix. The intrinsic parameters of camera are computed by the homography of scene plane to the image plane, finally a 4x4 non-singular matrix is evaluated, which satisfies the conditions of Euclidean reconstruction.... In this paper, we propose an algorithm for hierarchical reconstruction. The affine projection is used to approximate perspective projection; the projective depths are iteratively estimated by linear iterative method. And then, the projective reconstruction is obtained by SVD of the measurement matrix. The intrinsic parameters of camera are computed by the homography of scene plane to the image plane, finally a 4x4 non-singular matrix is evaluated, which satisfies the conditions of Euclidean reconstruction. This matrix can transform projective reconstruction into Euclidean reconstruction. The result indicates our method is efficient and robust. 提出一种分层三维欧氏重构算法。以仿射投影来逼近透视投影,采用线性迭代估计射影深度,通过测量矩阵的奇异值分解实现射影重构。然后利用场景中的平面与像平面的单应矩阵关于摄像机内参数的约束来实现摄象机的标定,最后求解一个满足欧氏重构条件的4×4非奇异矩阵,通过此矩阵将射影重构变换为欧氏重构。实验结果表明该算法是行之有效的。 In computer vision, it is called multi views 3D reconstruction for recovering both camera and object shapes from multiple images, and it is currently a topic of lively interest. A hierarchical reconstruction method had introduced in 1996. In the course of completing the hierarchical reconstruction, the projective reconstruction is the first key step, having very important effect on the precision of euclidean reconstruction. The existence methods are very efficient for simulation data, but they are not... In computer vision, it is called multi views 3D reconstruction for recovering both camera and object shapes from multiple images, and it is currently a topic of lively interest. A hierarchical reconstruction method had introduced in 1996. In the course of completing the hierarchical reconstruction, the projective reconstruction is the first key step, having very important effect on the precision of euclidean reconstruction. The existence methods are very efficient for simulation data, but they are not perfect for real image. Namely, they are not robust and the reliable results can only be obtained if images match accurately. In this paper, the projective reconstruction based on genetic algorithms is proposed, the projective depths are coded by using decimal system and the adaptability function is defined by a constraint of the measurement matrix rank 4. The projective depths are iteratively estimated by genetic algorithms and Singular value decomposition (SVD) so that the measurement matrix is made to be as close as possible to rank 4, and then the projective reconstruction is realized. The validity and robusticity of the proposed algorithm is confirmed by experiments. 在实现分层重构的过程中 ,射影重构是关键的第 1步 .目前 ,大多已有算法对模拟数据是非常有效的 ,但对于真实图象效果并不理想 .为了寻求更为鲁棒的算法 ,提出了一种基于遗传算法的射影重构算法 .该算法对于射影深度采用十进制编码 ,并以测量矩阵的秩为 4作为约束 ,来定义适应度函数 ,然后利用遗传算法 ,并结合奇异值分解 (SVD)技术来迭代估计射影深度 ,进而实现射影重构 .实验表明 ,该算法是行之有效的 ,且鲁棒性较好 << 更多相关文摘
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