kzcl

Unix系统下，小波包变换的实现源码实例，，当然，稍作改动也可用于windows系统下。 (Unix systems, the instance of the wavelet packet transform source, of course, some minor changes can also be used in the windows system.)

在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

说明：  基于双边滤波的retinex算法，暗光图像增强，有效去噪、提高对比度、去光晕；含论文和讲解PPT、实例代码(Retinex algorithm based on bilateral filtering can enhance dark image, effectively denoise, improve contrast and eliminate halo)

该程序为直方图均衡化程序，附有GUI界面，操作简单，可现实处理前后图像对比及处理前后的直方图分布情况显示，使用方便。(The program for histogram equalization program, with GUI interface, simple operation, the histogram distribution around the reality that is processing the image before and after treatment, easy to use.)

运用最小二乘和一范数约束求解低分辨率多光谱图像和高分辨率全色图像融合成高分辨率多光谱图像，程序附带主流的图像融合评价标准，需要融合的图像，对比图像；以及附带程序所需的稀疏包(Using least squares and a norm constraint solving low-resolution multi-spectral image and panchromatic image fusion into a high-resolution multi-spectral images, the program comes with mainstream image fusion uation criteria require the integration of image contrast image and Sparse package comes with the program required)

在目标图像中找出已经给出的训练图像。通过训练训练图像得到参数，来找出目标图像中的训练图像(Has been given in the target image to identify the training image. TRAINING image parameters to identify the training images of the target image)

编译好的tesseract 4.0库 for VS2015 (含lib、dll和头文件)，另附一个tessreact 4.0进行图片识别的示例程序，并给出运行具体配置方法！

基于meanshift算法最全面的资料收集，是本人长期收集该算法的结晶，里面有均值漂移算法的word文档，PPT资料，基于meanshift的目标跟踪算法（MATLAB），还有相应的文章，下了绝不后悔，将心比心，互惠互利。(Meanshift algorithm based on the most comprehensive data collection, is a long time, I collected the crystallization of the algorithm, which has mean-shift algorithm word document, PPT data, meanshift target tracking algorithm based on (MATLAB), there is a corresponding article, in no way under the regret, Care, and mutual benefits.)

大连理工大学卢湖川教授团队显著性检测方法4(Visual Tracking via Probability Continuous Outlier Model)

利用稀疏表示进行图像去噪时，需要生成两种学习字典，该源码比较了生成的两种字典的优劣(Denoising sparse representation, it is necessary to generate two learning dictionary, which compares the generated source code of the merits of two dictionary)