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在simulink下封装的单高斯模型的s_function.用于模型模块的搭建-Under the package, the simulink model of a single Gaussian s_function. For the model to build the module

用VTK与VC++混合编程实现raycasting方法对人头部数据的体绘制，内含人脑数据-with VTK and VC mixed programming method raycasting head on the body of data mapping, Data containing the human brain

活动轮廓模型(snake模型)的VC++源码，可以编译并且运行-Active contour model (snake model) VC++ Source, you can compile and run

数字图像处理常见算法的源程序，形态学，二值化，高斯噪音都包括了 -Common digital image processing algorithms source, morphology, thresholding, Gaussian noise is included

自己编的pca和IHS融合代码!! 与大家交流!-own addendum to the pca and IHS integration code! ! Share with you!

针对传统方法难以可靠估计图像中纹理单一区域像素点视差的问题,将纹理分析应用于立体匹配中,提 出图像分块整体匹配的方法。首先用LBP/C纹理分析方法对图像纹理进行描述 然后进行基于区域生长的扩张检 测,得到纹理单一图像块 最后对图像块进行整体匹配,得到纹理单一区域的稠密视差图。对国际标准图像进行测试, 结果表明该算法能提高纹理单一区域稠密视差图的精度,具有实用价值 -Due to the difficulty of getting disparity of less textured pixels with traditional approach, block matching algorithm with texture analysis was put forward. First, the images were described by LBP/C texture analysis. Then the expansion detection based on region growing was performed, and the less textured blocks were acquired. Finally the dense disparitymap of less textured area was obtained through block matching. The proposed algorithm was tested with the international standard image data and comparedwith some existing algorithms. The experimental results show that the proposed algorithm can improve the accuracy of disparitywhen handling less textured areas and can be used in practic

基本图像处理的VC6源码,内有详细的代码说明和图像效果-Basic image processing VC6 source, which has a detailed code description and image

基于神经网络的识别程序，程序采用三层BP网络训练，采用了普通定义中加入动量因子的训练法，精确度很高。-based on neural network identification procedures, procedures adopted a three-tier network training BP, with its definition of momentum into the training factor, a high degree of precision.

高斯平滑是数字图象处理中的一种重要手段,VC++程序,可直接测试.-Gaussian smoothing digital image processing of an important means, VC procedures can be directly tested.

图像灰度拉伸、直方图均衡算法以及FCM聚类算法。-Image stretching, histogram equalization algorithm and the FCM clustering algorithm.