提取图像的局部二值特征 LBP matlab

采用小波变换提取图像特征，对图像特征点进行匹配可以实现畸变图像校正。(Using wavelet transform to extract image features, the image feature points matching can be achieved distortion image correction.)

Timesat3.2，用于遥感影像的NDVI时间序列分析，提取物候参数。 (Timesat3.2, NDVI time series for the analysis of remote sensing images, extract phenological parameters.)

说明：  INSAR相位解缠算法，使用的python语言，枝切法进行相位解缠。(INSAR phase unwrapping algorithm, using python language, branch cut method for phase unwrapping.)

图像 自动 标注 通过分析 训练集 ，学习 图像 和标注词 之间的关系，得出 之间的关系，得出 一些规则， 然后使用这些规则 自动 推导出 图像 最适合的标注词 ，这些标注词代表了图像的 ，这些标注词代表了图像的 高层 语义信息 。(Automatic image annotation by analyzing the training set, learning image and mark the relationship between words, the relationship between the draw, draw some rules, and then use these rules to automatically derive the most suitable image annotation words, these words represent the image annotation , these words represent the image annotation level semantic information.)

基于MALTAB实现的舌象纹理特征提取，程序不大但是使用(ongue like texture feature extraction matlab implementation tongue like texture feature extraction matlab implementation)

He met her at a party. She was outstanding; many guys were after her, but nobody paid any attention to him. After the party, he invited her for coffee. She was surprised. So as not to appear rude, she went along.

数字图象处理，模式识别，人脸识别，包括了程序和一个人脸库，是相当好的学习东西(Digital image processing, pattern recognition, face recognition, including the procedures and a face database is very good to learn things)

说明：  opencv基础教程，对于初学者来说是个不错的选择。(opencv basic tutorial for beginners is a good choice.)

KSVD&&OMP处理图像的程序，有演示效果(KSVD&&OMP )

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)