matlab

说明：  图像分割加特征提取的算法，一起分享，共同研究，共同进步(Add feature extraction image segmentation algorithms, along with sharing, joint research, and common progress)

二维窗口函数，包含汉宁窗，布莱克曼窗，契比雪夫窗等(it creates a 2 dimentional window for a sample image)

说明：  Calculate the Laplacian of an image, can be accelerated by the GPU.

本程序实现位图的读写，均衡化，滤波，LBP，LTP特征提取以及基于LBP，LTP和卡方距离，最近邻分类的人脸识别，批量给图片改名等功能(The program can read and write a bitmap, equalization, filtering, LBP, LTP feature extraction and nearest neighbor classifier for face recognition based on LBP, LTP and chi-square distance, rename picture and some other functions.)

图像质量评价，计算两幅图像的保真性，鲁棒性 ，结构相似性 (Image quality assessment，calculate the fidelity ，robust and structural similarity of the image，)

实现了三种图像匹配算法 1：归一化互相关匹配算法 2：基于Hausdorff距离的图像匹配算法 3：图像不变矩匹配算法(Implements three image matching algorithm 1: normalized cross correlation matching algorithm 2: image matching algorithm based on Hausdorff distance 3: The image matching algorithm invariant moments)

基于灰度的两幅图像配准算法，两个程序，可以实现相关系数和PFC算法(Based on Gray' s two image registration algorithm, the two procedures, the correlation coefficient can be achieved and the PFC algorithm)

介绍matlab下求解偏微分方程的函数和用法(Introduction matlab function for solving partial differential equations and usage)

用于学习经典书籍《合成孔径雷达算法与实现》(to learn the book named<<digital processing of SAR Algorithms and implementation>>)

本文主要研究基于水平集方法的活动轮廓模型图像分割，在回顾活动轮廓模 型发展的基础上，介绍了曲线演化理论及其水平集方法，证明了水平集方法可以 有稳定的数值实现方式且其处理拓扑变化的自然性，进一步引出了变分水平集方 法。 本文介绍了两种不同的几何活动轮廓模型：基于梯度信息的李纯明模型以及 基于区域信息的 Chan-Vese 模型（C-V 模型）。在分析上述两模型的优缺点上，提 出了一种改进的 C-V 模型，改进模型引入了距离约束项，同时对基于区域的外部 能量项进行了改进，使得水平集函数重新初始化寓于模型的演化之中，分割结果 对参数的依赖性减小。 (This paper mainly study image segmentation of active contour model based on level set. After reviewing the development of the active contour model, curve evolution and level set method are presented, then we proved that the level set method has a stable numerical realization and can describe the topology change of the contour naturally, in addition we introduce level set of variational method. We introduce two different GAC models in this paper: Li Chunming model which is based on gradient information and Chan-Vese model （C-V model） which is based on region information. Based on the drawbacks and advantages of these two models, we improve the C-V model by adding penalizing energy term of signed distance function, at the same time, mending the external energy term which based on region information, then the re-initialization of the level set can accomplish in the model evolution, and the degree of the segmentation result depend on the parameters decline. )