Batch process to narrow the picture for web site development

Fuzy K Means 快速图象分割Fuzy K Means Fuzy K Means-Fuzy K Means Fuzy K MeansFuzy K MeansFuzy K MeansFuzy K MeansFuzy K Means

图像特征提取，包括：颜色直方图，颜色矩，边缘直方图，Gabor小波变换，局部二值图，GIST。-Image feature extraction, including: color histogram, color moment, the edge histogram, Gabor wavelet transform, partial binary image, GIST.

利用GAC水平集迎风方案实现图像分割，分割效果较好，收敛速度快-GAC sets the level of use of the wind program to achieve image segmentation, segmentation better, faster convergence

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。-simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.

c#在图像处理的方面的源程序：对图像进行灰度化处理。一个很简单的程序，适合刚刚使用C＃进行图像处理者借鉴～-c# deal with aspects of the image source: gray-scale image processing. A very simple procedure, suitable for just using C# for image processing are drawing ~

对一幅彩色图像进行离散DCT变换,并且选择合适的阀值进行DCT反变换-Color of an image of discrete DCT transform, and select the appropriate threshold for DCT Transform

这是简单的图像分割程序，用VC编写-It is a simple image segmentation procedures, the preparation of VC

基于复杂背景的人体手势识别，以及对它的鲁棒性的测试-Based on complex background of human gesture recognition, as well as its robustness test

对图像进行遥感伪彩色处理的源代码，编译后的可执行文件须带参数运行-of remote sensing images pseudo-color processing of source code, the compiled executable file parameters required to bring Operation

基于kmeans的图像分割算法，首先得到图像，再确定分类，并计算各分类的均值，从而得到各个像素的分类。-Kmeans-based image segmentation algorithm, first of all get the image, and then determine the classification, and calculate the various categories mean, and thus obtain the classification of each pixel.