Chapter4

利用高斯金字塔原理进行连续的图像序列的压缩。需要将图像序列连续命名。输出为压缩图像。(Gaussian pyramid principle compress continuous image sequences. Need the image sequence continuously named. Output is compressed image)

图像修复算法 及 仿真程序代码 2011年《基于纹理特征的自适应图像修复算法》(the image inpainting algorithm and its matlab code “Adaptive Image Inpainting Algorithm Based On the Texture Feature” )

对两幅图像提取特征点，并计算基本矩阵，对两幅图像进行极线矫正，并画出校正后图像特征点的极线，效果非常好(The two images of the image feature points in the feature points are extracted, and the calculated fundamental matrix, correction of lines of the two images, and after the draw correction lines, very good results)

Source code to segment skin colour.

gray image to color image conversion

这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值！！( ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techniques are available: - Principal Component Analysis ( PCA ) - Linear Discriminant Analysis ( LDA ) - Multidimensional scaling ( MDS ) - Probabilistic PCA ( ProbPCA ) - Factor analysis ( FactorAnalysis ) - Sammon mapping ( Sammon ) - Isomap ( Isomap ) - Landmark Isomap ( LandmarkIsomap ) - Locally Linear Embedding ( LLE ) - Laplacian Eigenmaps ( Laplacian ) - Hessian LLE ( HessianLLE ) - Local Tangent Space Alignment ( LTSA ) - Diffusion maps ( DiffusionMaps ) - Kernel PCA ( KernelPCA ) - Generalized Discriminant Analysis ( KernelLDA ) )

本代码是完成傅里叶梅林变换对图像的旋转剪切。没有嵌入水印。(The code is complete Fourier Mellin transform of the image rotation shear. No embedded watermark.)

二维地震信号的emd实现，逐道分解，挑选信号主导分量(EMD realization of two-dimensional seismic signal, decomposition by channel, selection of signal dominant component)

边缘检测算法的比较，有susan算子，log算子，prewitt算子(Comparison of edge detection algorithm, there is susan operator, log operator, prewitt operator)

说明：  彩色图像分割，灰常不错。This function implements kmeans clustering on an input RGB (m x n x 3) image. The user inputs at least two inputs: IMGIN and NCLUSTERS, and this function will step through an interactive color segmentation using kmeans clustering. It is interactive in that the user will be prompted to click-define the target colors in the segmentation.(This function implements kmeans clustering on an input RGB (m x n x 3) image. The user inputs at least two inputs: IMGIN and NCLUSTERS, and this function will step through an interactive color segmentation using kmeans clustering. It is interactive in that the user will be prompted to click-define the target colors in the segmentation.)