节理面粗糙度风玫瑰图

图像重采样，根据新旧图像像元大小的比例进行图像的重采样，(This function resamples the images at the new grid points defined by the new pixel sizes. It assumes that intensities are defined at the pixel centers)

实现高斯模糊，运动模糊等的点扩展函数psf的估计，并复原(The estimation of point spread function PSF, such as Gauss blur and motion blur, is realized and restored.)

说明：  实现图像受混合噪声污染时去除噪声的目的，在6种方法比较之下，得出最优的图像去噪算法(Image by mixed noise pollution when the purpose of removing noise, in the six kinds of methods of comparison, the optimum image denoising algorithm)

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。 该方法于 1999 年由 David Lowe 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。(来自百度)(SIFT, namely Scale-invariant feature transform (SIFT), is a description used in the field of image processing. This description has scale invariance and can detect key points in images. It is a local feature descriptor. This method was first published by David Lowe at the International Conference on Computer Vision (ICCV) in 1999 and published in the International Journal of Computer Vision (IJCV) in 2004 after it was sorted out and perfected by David Lowe again. As of August 2014, more than 25,000 citations have been cited. (From Baidu))

基于模板匹配的人眼定位算法，代码简单易懂，定位精度高(The eye location algorithm based on template matching, the code simple, high positioning accuracy)

ebook on Jtag and boundary scan

matlab6.5图形图像处理源程序 部分内容 第二章压缩包 (matlab6.5 )

说明：  最小噪声分离变换（Minimum Noise Fraction Rotation，MNF Rotation）工具用于判定图像数据内在的维数（即波段数），分离数据中的噪声，减少随后处理中的计算需求量(Minimum Noise Fraction Rotation (MNF Rotation) tool is used to determine the intrinsic dimension (i.e. band number) of image data, separate the noise in data, and reduce the computational requirements in subsequent processing.)

说明：  使用matlab实现脑磁共振图像的海马体分割(Using MATLAB to segment the hippocampus of brain MR images)

基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大(content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of similarity : Mahalanobis distance, the Euclidean distance relevance feedback : machine learning methods, such as SVM. Neural network search and classification : two very similar samples from the small, although the two are similar to the samples may not be much distance)