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SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

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：  此代码可以进行指纹识别，包括预处理，特征提取，(This code can be used for fingerprint identification, including preprocessing, feature extraction,)

当前流行的机器学习算法之一：boosting的变体——Gentleboost(The current popular one of machine learning algorithms: boosting variants- Gentleboost)

基于椒盐噪声滤波的改进加权中值滤波算法,目的在于处理高浓度椒盐噪声，并且有效的保护边缘细节(salt and pepper noise filter and weighted)

自动种子区域生长代码，用MATLAB运行，不必选择种子点 可以自动选择阈值 实现图像分割！(The automatic seed region growing code, run, do not have to select seed point can automatically select the threshold image segmentation using MATLAB)

Segmentation Algorithm

由VC实现画矩形、椭圆和随手画功能，对图像处理人员会有帮助(paintings by VC rectangular, oval and hand painting functions, image processing staff to be helpful)

说明：  在图像分割中，利用Hough变换进行直线提取的matlab源程序。提取方法不同于一般的边缘检测算法。(In image segmentation using Hough transform for straight line extraction matlab source. Extraction method is different from the general edge detection algorithm.)

该文件包含高斯高通滤波的源代码和高斯低通滤波的源代码，可以运行，效果很好(Gaussian high-pass filter Gaussian low-pass filtering )