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改进的图像分割算法：运用SLIC超像素分割和DBSCAN聚类算法相结合的方法实现图像分割(Image Segmentation using SLIC SuperPixels and DBSCAN Clustering)

这是一种对TV模型算法的一种快速改进算法，C++实现，在Code::Blocks下编译成功(This is a TV model algorithm for a fast algorithm, C++ achieve, in the Code :: Blocks compile successfully under)

该文件涵盖了65个使用IDL进行图像处理的源码例子，对IDL语言以及ENVI软件的理解非常有帮助。(The document covers 65 for image processing using IDL source code examples, on IDL and ENVI software' s understanding of language is very helpful.)

Tarel和Hautiere的图像去雾方面的程序，去雾效果很不错，对于研究很在参考价值。(Tarel and Hautiere image to the fog of program, go to mist effect very good, to study in a reference value. )

说明：  为了在抑制噪声时尽可能保留图像边缘信息，提出一种自适应各向异性高斯滤波器设计方法. 该方法由独立 强度传播（IDS）模型决定滤波器的长轴尺度，由像素的邻域平滑度决定长短轴的比例，然后根据该处的灰度梯度方 向自适应决定各向异性滤波器的长轴方向. 仿真实验表明，提出的自适应各向异性滤波器具有很强的噪声抑制和 边缘保持能力. (In order to suppress noise, as far as possible to keep the image edge information, an adaptive anisotropic Gaussian filter design methods. The method by the independent strength spread (IDS) model determines the long axis of the filter scale, the pixel neighborhood smoothness determine the ratio of minor axis, and then there' s gray gradient direction of adaptive decision the long axis of anisotropic filter. Simulation results show that the proposed adaptive anisotropic filter has a strong noise suppression and preserve edges.)

这是我自己写的关于 图像处理中retinex、SSR、自适应的对比度增强算法的模板，有很好的效果 希望对你们有帮助( this is for retinex module, I wish it can help for you )

这是一篇对于SIFT算法应用于非刚性图像配准的论文，相当不错(This is one for the SIFT algorithm is applied to non-rigid image registration of papers, quite good)

基于snake模型的图像分割.Matlab 源程序(Snake model based on image segmentation. Matlab source code)

ICP模型配准有二维三维两种方式，简单好用(ICP RIGID EASY 2 DIMENSION 3 DIMENSION)

该源码为目标检测和跟踪算法 包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)