Texture

图像增强里的小程序，有中值滤波，平滑处理还有频域增强(Image Enhancement in the small procedures, median filtering, smoothing also enhanced the frequency domain)

EM算法是最为经典的统计图像分割方法，代码基于matlab，使用EM算法实现图像分割,源代码加实例图像，非常好用(By using EM algrithm to realize the segementation of image)

高斯平滑滤波实例，高斯滤波实质上是一种信号的滤波器，其用途是信号的平滑处理，我们知道数字图像用于后期应用，其噪声是最大的问题，，由于误差会累计传递等原因，很多图像处理教材会在很早的时候介绍Gauss滤波器，用于得到信噪比SNR较高的图像（反应真实信号）。 (Instance of the Gaussian smoothing filter, Gaussian filter is essentially a signal filter, its purpose is to smooth processing of the signal, we know that the digital image for the late application, the noise is the biggest problem, the error accumulated transfer reason, a lot of image processing textbooks will be introduced at an early Gauss filter for higher SNR of the image (real response signal).)

本代码为了检测哈弗曼编码后的图像压缩比，并且给出了哈夫曼的编码与解码(The code in order to detect Huffman encoded image compression ratio, and gives Huffman encoding and decoding)

说明：  可以获取连通域的最小外接矩形，然后做些其他处理(You can get the minimum external rectangle of a connected domain,and you can do something else.)

有关图像处理中的图像校正，MATLAB，亲测可行(ImageRegistration matlab)

该源代码可以直接下载下来使用，而且是地地道道的对RGB图像进行分割，我已经测试通过了。(The source code can be directly downloaded to use, but also out-and-out on the RGB image partition, I have the test passed.)

视频图像处理，采用三帧差分法进行视频前景和背景的分离(Video image processing, inter-frame difference method of separating the foreground and background video)

图像校正的Matlab代码，非常不错，可以实现很好的校正结果，速度也很快(Image correction of Matlab code, very good, very good correction can be achieved the result of quick)

本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过 大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、 立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on the Python language.A large number of examples carefully analyze object recognition, content based image search, optical character recognition, optical flow, tracking, 3D reconstruction,Stereoscopic imaging, augmented reality, attitude estimation, panoramic creation, image segmentation, noise reduction, image grouping and other techniques.)