image-processing

说明：  本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.)

楔波变换，在SAR图像中实现边缘检测，为目标识别做铺垫工作。(The wedge waves transform, SAR image edge detection, target identification pave the way for the work.)

MODIS图像批量镶嵌拼接方法（IDL/ENVI）(modis mosaic)

说明：  基于随机游走的图像分割算法，传统的随机游走算法实现图像分割(Image segmentation algorithm based on random walk)

实现了车辆目标的背景建模、前景检测、阴影消除、车辆跟踪(Achieve a vehicle target background modeling, foreground detection, shadow elimination, vehicle tracking)

说明：  图像滤波，中值滤波，自适应，对比结果，图像去燥使用(Image filtering, median filtering, adaptive, comparative results, medical image use)

贝叶斯分类程序，用于图像的分割，二值化等，是模式识别中的一种基本分类方法。 k均值聚类，神经网络(Bayesian classification procedures for image segmentation, binarization is a basic classification in pattern recognition. k-means clustering, neural networks)

L0梯度最小化算法，2011年由xuli提出的算法(L0 gradient minimization algorithm, in 2011 xuli put forward the algorithm )

这是三维点云数据边缘线提取的c++算法，适合于LIDAR数据的三维处理，供大家参考学习(This is 3 d point cloud data edge extraction of c++ algorithm, suitable for LIDAR data 3 d processing, provide everyone reference to study)

IDL计算影像均值、标准差、偏差指数、信息熵、平均梯度、光谱扭曲程度、相关系数的小程序。(average gradient, correlation coefficient, entropy, cross entropy, joint entropy, mean square error and mutual information, signal to noise ratio, peak signal to noise ratio, )