SVD method ensured the algorithm's robustness.
SVD分解保证了算法的鲁棒性。
Parallel Lanczos SVD (Singular Value Decomposition) solver.
并行LanczosSVD(奇异值分解)计算。
The paper brings forward a kind of hybrid SVD-DCT zero-watermark algorithm.
提出了一种结合奇异值分解（SVD）和DCT变换的零水印算法。
Singular value decomposition (SVD) has very important applications in image processing.
奇异值分解(SVD)在图像处理中具有极其重要的应用。
After tested, both SVD statistical model and bulk formula can get the information of atmosphere.
经验证，SVD统计模式及块体公式均可抓住大气的基本信息。
A mixed method based on SVD-QR and deformable template is proposed to extract lipreading feature.
提出了SVD—QR和变形模板相结合的混合特征提取算法。
A new method for sinusoidal detection is presented based on the SVD of the observation data matrix.
本文以对观测数据矩阵直接进行奇异值分解为基础提出了一种正弦检测的新方法。
Using the SVD technology, the features of the images were extracted, and the eigenvector was built.
采用SVD技术提取图像特征，构建特征向量和盲隐写分析判决函数。
Using image segmentation, a novel watermarking algorithm based on image segmentation and SVD is proposed.
利用图像分割法，提出了基于图像分割和SVD的数字水印算法。
Image filtering based on SVD favors the denoising in the line (horizontal) and column (vertical) direction.
基于奇异值分解滤波可以有效地分析水平(垂直)方向的图像特性。
The problem of image matching and target tracking based on singular value decomposition (SVD) was discussed.
研究了基于奇异值分解的图像匹配和目标跟踪问题。
Thetrick in using SVD is in figuring out how many dimensions or"concepts" to use when approximating the matrix.
利用SVD的技巧就是去找到用多少维度（概念）去估计这个矩阵。
A face identification method based on singular value decomposition (SVD) and data fusion is proposed in this paper.
提出了一种基于奇异值分解和数据融合的脸像鉴别方法。
Finally, on the basis of studying single digital watermarking, proposes a double digital algorithm based on SVD and DWT.
最后，在研究单数字水印的基础上，提出了一种基于SVD和DWT的双数字水印算法。
The Singular Value Decomposition (SVD) method for the equilibrium matrix is developed and a physical explanation is given.
引入了平衡矩阵的奇异值分解（SVD）方法并解释了其力学含义。
Assessment results show that the SVD technique can obviously improve identification accuracy as compared to the PI solution.
结果表明，使用奇异值解法能够明显提高频时域法的识别精度。
Discussing the implementation of the CORDIC-SVD in FPGAs and its applications in the Eigen-subspace method's implementation.
讨论了利用FPGA实现CORDIC - SVD的方法及如何将其应用于特征子空间法的实现中。
An algorithm based on singular value decomposition (SVD) is proposed, which hides secret information in singular value vector.
给出了一种基于矩阵奇异值分解(SVD)和奇异值量化的信息隐藏算法。
An improved SVD method was introduced by combining empirical orthogonal function (EOF) with singular value decomposition (SVD).
将奇异值分解同自然正交分解相结合，提出一种改进的正交奇异值分解方法。
A face recognition method based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD) is presented.
提出了奇异值分解(SVD)和主分量分析(PCA)相结合的人脸识别算法。
In this paper, also the primary applications of EOF and SVD in analyzing satellite altimeter data of sea surface wind and wave is studied.
本文还就经验正交展开和奇异值分解方法在卫星高度计海面风、浪资料分析中的应用作了初步探时。
A general difficulty of using singular value decomposition (SVD) to split signal and noise subspaces is in the right choice of effective rank.
用奇异值分解界定信号和噪声子空间的困难之处，在于有效秩的确定。
In addition, a method based on singular value decomposition (SVD) was proceed to deal with the obtained result for dropping influence of noise.
为降低噪声的影响，采用一个基于奇异值分解(SVD)的方法对识别的结构进行处理。
Results show that iterative refinement using the SVD can improve regression coefficient estimates in the cases where the design matrix is highly collinear.
结果表明，在设计矩阵高度共线性时，用奇异值分解的迭代加细可以改进回归系数的估计。
After obtaining correct projective depths, we decompose the measurement matrix into camera motion in projective space and projective reconstruction by SVD.
在获得正确的射影深度后，通过奇异值分解将测量矩阵分解为射影空间下的摄像机运动和物体三维几何形状(射影重构)。
Digital image is transformed into singular value matrix that contains non-zero singular values by singular value decomposition (SVD), the image is compressed.
通过对图像进行奇异值分解，将一幅图像转换成只包含几个非零值的奇异值矩阵，实现图像压缩。
Then, the performance of two SVD algorithms and feature subspace radar target recognition algorithm based on SVD are evaluated according to real data of planes.
进而将两种SVD算法应用在特征子空间雷达目标一维距离像识别法中，使用实测数据对其进行速度验证和性能评估。
Thereason SVD is useful, is that it finds a reduced dimensional representation ofour matrix that emphasizes the strongest relationships and throws away thenoise.
SVD非常有用的原因是，它能够找到我们矩阵的一个降维表示，他强化了其中较强的关系并且扔掉了噪音（这个算法也常被用来做图像压缩）。
Thereason SVD is useful, is that it finds a reduced dimensional representation ofour matrix that emphasizes the strongest relationships and throws away thenoise.
SVD非常有用的原因是，它能够找到我们矩阵的一个降维表示，他强化了其中较强的关系并且扔掉了噪音（这个算法也常被用来做图像压缩）。
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