搜索资源列表
NPE
- 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embeddi
lorenz_ext
- m为嵌入空间维数 tau为时间延迟 data为输入时间序列 N为时间序列长度 X为输出,是m*M维矩阵- M for the embedding space dimension tau is the time delay for the input data time series N for the time series length of X to output, is m* M-dimensional matrix
DCT_WaterMark
- 使用C实现在DCT域嵌入水印和提取水印。对矩阵操作要求比较高-The realization of the use of C in the DCT domain watermark embedding and extraction of the watermark. Operating requirements of the matrix is relatively high
Textureandshapeinformationfusionforfacialexpressio
- 三维人脸识别的经典文章，也是三维人脸表情识别引用极多的文章，被模式识别杂志收录-Texture and shape information fusion for facial expression and facial action unit recognition Keywords: Facial expression recognition Facial Action Unit recognition Discriminant Non-Negative Matrix Factor
rp
- 对序列进行重构后，计算对应的矩阵，针对不同的嵌入参数，得到不同的图形-Reconstruction of the sequence, the calculation of the corresponding matrix, for different embedding parameters, get a different graphics
lle
- Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with man
GSM-voice-cipher
- 一种基于GSM的低码率语音信息隐秘传输方法。 本文描述了一种可将一路低码率2．4kb／s混合激励线性预测(MELP)编码语音信息，隐藏在另一路13kb／s的GSM编码语音中，通过公共信道隐秘传输的方法。文中给出了一种新的数据嵌入方法，该方法以一个单位增广矩阵为基础，可在(2L+1)比特可修改信息中嵌入2L比特数据信息，而最多只需修改￡比特宿主信息，有较高的数据嵌入率，算法计算复杂度较低，较易于硬件实现，且对宿主信息的影响也较少。-GSM-based low bit rate voice co
new-f5-stego
- The F5 algorithm embeds message bits into randomly-chosen DCT coefficients and employs matrix embedding that minimizes the necessary number of changes to embed a message of certain length.
annlyap
- 最小RMSE神经网络方法计算Lyapunov指数的matlab函数。-This M-file calculates Lyapunov exponents with minimum RMSE neural network. After estimation of network weights and finding network with minimum BIC, derivatives are calculated. Sum of logarithm of QR decomposition
2012--Improving-the-embedding-efficiency-of-weigh
- Improving the embedding efficiency of weight matrix-based steganography for grayscale images
LGME
- input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: ground
RdwtSvdWatermarking
- 将原始载体图片经过一维冗余离散小波变换分成四个子频带，分别对每个子频带进行奇异值分解， 将水印图片灰度值直接按比例缩小嵌入到奇异值矩阵中，再将此矩阵进行一次奇异值分解得到奇异值当做此子带的奇异值， 重建此子带矩阵，而后将进行反冗余离散小波变换得到嵌入载体后的水印图像。-The original image after the one-dimensional vector redundant discrete wavelet transform is divided into four subba
icml2010-code(2)
- Power Iteration Clustering projection Input: W - row-normalized affinity matrix v0 - starting vector conv - convergence threshold maxit - maximum number of iterations Output: vt - 1-d PIC embedding i - iterations ran
sangarticle
- Matlab实现的一种基于矩阵分解的信息隐藏代码-This code realize the function of embedding the secret information based on the matrix decomposition
f5
- Many steganographic systems are weak against visual and statistical attacks. Systems without these weaknesses offer only a relatively small capacity for steganographic messages. The newly developed algorithm F5 withstands visual and statistical
LSB-for-QR-Code
- 在最不重要位替换嵌入算法的基础上引入矩阵编码，增强水印隐蔽性，有效地提高水印嵌入效率。-On the basis of the least significant bit embedded algorithm is introduced to Replace the matrix coding, to enhance hidden watermark, the watermark embedding effectively improve efficiency.
nsf5_simulation
- nsf5隐写算法，DCT的升级版，MATLAB实现，可以抵抗一般的DCT隐写分析(The algorithm nsF5 evolved from the F5 algorithm originally proposed by Andreas Westfeld in 2001. F5 decreases the absolute value of DCT coefficients and incorporates matrix embedding - a coding scheme that de
PCA程序
- 实现主成分分析 % Usage: % [eigvector, eigvalue] = PCA(data, options) % [eigvector, eigvalue] = PCA(data) % % Input: % data - Data matrix. Each row vector of fea is a data point. % % options.ReducedDim