图像矩阵 meaning in Chinese
image matrix
Examples
- After that , a svd of polar seal image matrix method is mentioned to extract vsv features
提出了利用极坐标图像矩阵进行奇异值分解的方法以提取不变奇异值特征。 - The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors . that is to say , if we use them to deal with the image recognition problem , the first step is to transform original image matrices into same dimensional vectors , and then rely on these vectors to evaluate the covariance matrix and to determine the projector
所提出的这两种方法的共同特点是，在进行图像特征抽取时，不需要事先将图像矩阵转化为高维的图像向量，而是直接利用图像矩阵本身构造图像散布矩阵，然后基于这些散布矩阵进行主分量分析与线性鉴别分析。 - Based on the svs characteristic analysis of image matrix , a visually recognizable binary image watermark is embedded into maximal singular value coefficient in block - based svd transform domain of the cover image . here we brought forward two primary schemes : one need original signal and the other is blind ( without the original cover ) . experimental results show that our schemes can extract reliable copy of the hidden watermark from images that have been significantly degraded or altered through several common geometric distortions and signal processing operations
本文基于图像矩阵的svd奇异值分解特性分析，提出了在新的svd变换域中进行的数字水印算法，水印信息嵌入到分块变换的最大奇异值分量系数中，应用混沌变换加密和空域置换，改善了空域性能，安全性高；利用图像分块矩阵的奇异值分解稳定性好的特点，采用图像内容自适应方法计算水印的嵌入强度，增强了算法的稳健性；采用二值图像作为有意义水印进行嵌入和检测，水印在感知上是可视的。