A-kind-of-image-texture-segmentation

Curvelet变换进行图像去噪，Matlab7.0程序(Curvelet transform for image denoising, Matlab7.0 procedures)

用delaunay三角剖分和网格填充对离散点云数据进行网格重建(Mesh reconstruction of discrete point cloud data using Delaunay triangulation and mesh filling)

分段函数的图形画法。用matlab画分段函数，并有图形结果，大家可根据需要修改方程(Sub-function graphic painting. Matlab piecewise function with the painting, and graphic results, we can modify equation)

利用Artoolkit实现了虚拟现实的算法，VS2008下测试通过(Use Artoolkit algorithm to achieve a virtual reality, VS2008 under test)

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.)

说明：  批处理一批图片，实现对其中的图像提取几何特征参数。(Batch number of pictures, images of which the realization of the geometric characteristic parameter extraction.)

基于多尺度超像素的显著性检测代码和文章(Saliency Detection with Multi-Scale Superpixels)

数字图像相位相关性，对两幅图像位移的前后相位变化进行检测(Phase Correlation)

canny算法分割，本算法通过canny对图像进行分割，并提取边缘。()

这是我本科的毕业设计做的关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。(This is my undergraduate graduation design done on the medical images based on the shape of the image retrieval. Pretreatment with wavelet denoising, feature selection invariant moment.)