coderesult

说明：  读图然后根据图像的对象，进行边缘检测，椭圆拟合(Read the image and then carry out edge detection and ellipse fitting according to the object of the image)

VC.NET图象处理,采用基于CImage类的图象处理技术,封装的函数为基本的图象处理部分,如几何变换等等,开发环境为VC++.NET(VC.NET image processing, based on CImage kind of image processing technology, Packaging for the basic functions of image processing, such as geometric transformation, and so on, the environment for the development of the VC.NET)

最大最小距离算法matlab代码，注释比较详细(Maximum minimum distance algorithm matlab code)

用matlab基本语句实现直方图均衡化库函数histeq的功能(matlab histeq)

IMM算法，用于机动目标的精确跟踪和定位，该模型集考虑了三种典型子模型:CV,CT,CA. (interacting multiple model (IMM) code for target tracking )

产生运动模糊图像以及逆滤波，维纳滤波的恢复效果比较(Produce motion-blurred images, as well as inverse filtering, Wiener filtering effect of the restoration of)

说明：  识别指纹端点分叉点以及奇异点，其中还包括指纹方向场和指纹梯度场的计算(It also includes the calculation of fingerprint direction field and fingerprint gradient field)

C# 以任意角度旋转图片实例

图像处理实习（针对遥感影像），包含许多经典的图像处理功能，比如多种植被指数计算、小波变换、镶嵌、图像分类等(Image processing internship (for remote sensing images), contains many classic image processing functions, such as a variety of vegetation indices, wavelet transform, mosaic, image classification, etc.)

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. )