4885664

说明：  K均值算法，Matlab实现，输入分类数，实现分类(K means algorithm, Matlab implementation, enter the number of classifications to achieve classification)

采用K-SVD对图像进行去噪处理。可以对不同类型的图像进行去噪处理。(Image denoising using K-SVD)

基于matlab的图像处理边缘提取程序，包含各种常用的边缘提取方法。(Matlab image processing based on edge extraction process, contains a variety of commonly used edge detection methods.)

上传一张图片，这是在做图像水印研究时常用的一张lina图(image used in watermarking)

典型的LSB水印嵌入和提取算法，它是一个基本的matlab程序，可以帮助初学者入门 (typical LSB embedding and extraction algorithm, which is a basic Matlab procedures, Portal can help beginners)

何恺明等人研究出的基于暗通道的经典图像去雾算法，不仅可以还原图像的颜色和能见度，同时也能利用雾的浓度来估计物体的距离。(The classic fog removal algorithm based on dark channel, which was developed by He Kaiming and others, not only can restore the color and visibility of images, but also can estimate the distance of objects by using the concentration of fog.)

说明：  基于空间信息的模糊均值聚类算法，SFCM，适用于数据分析，图像分割(space fuzzy-c-means clustering algorithm)

这是一种实现数字图像拼接的surf算法的matlab程序，能够取得不错的效果。(This is a digital image mosaic surf algorithm matlab program, can achieve good results.)

说明：  基于CT切片的matlab分割程序，可以很好的分割CT切片图中的器官(Segmentation based on CT slices matlab program that can split a good map of the organ slice CT)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)