hist3d

超像素分割方法，在计算机视觉领域，图像分割（Segmentation）指的是将数字图像细分为多个图像子区域（像素的集合）（也被称作超像素）的过程。超像素有一系列位置相邻且颜色、亮度、纹理等特征相似的像素点组成的小区域。这些小区域大多保留了进一步进行图像分割的有效信息，且一般不会破坏图像中物体的边界信息。(Super pixel Segmentation method, in the field of computer vision, image Segmentation, Segmentation) refers to the digital image Segmentation region for multiple image sequence (the set of pixels) (also known as the pixels) process. Adjacent pixels have a series of position and features such as color, brightness, texture similar to that of small region composed of pixels. Most of these small area retains the further effective information for image segmentation, and generally does not destroy the boundary of the object in the image information.)

说明：  均值方差与协方差 协方差矩阵、特征值与特征向量的提取、PCA原理 人脸识别算法之EigenFace、LBPH 人脸识别应用开发案例(Mean Variance and Covariance Covariance Matrix, Extraction of Eigenvalues and Eigenvectors, PCA Principle EigenFace and LBPH of Face Recognition Algorithms Case Study of Face Recognition Application Development)

说明：  用flash手绘小人头像,简单清晰,可以用来制作个人头像.完全原创(Hand-painted villain with the flash head, simple and clear, can be used to create personal avatar. Completely original)

说明：  一个从网上找来的屏幕截取代码，需要的就看看吧(A screen from the Internet and got the interception code, need to look at it)

3维坐标变换应用于3维图像配准 要下的快啊(3-D coordinate transformation applied to 3-dimensional image registration should be under快啊)

说明：  分水岭算法是一种图像区域分割法，在分割的过程中，它会把跟临近像素间的相似性作为重要的参考依据，从而将在空间位置上相近并且灰度值相近的像素点互相连接起来构成一个封闭的轮廓，封闭性是分水岭算法的一个重要特征。本示例适用于二维和三维图像，计算分水岭变换并将结果标签矩阵显示为RGB图像。 分水岭算法是一种图像区域分割法，在分割的过程中，它会把跟临近像素间的相似性作为重要的参考依据，从而将在空间位置上相近并且灰度值相近的像素点互相连接起来构成一个封闭的轮廓，封闭性是分水岭算法的一个重要特征。本示例适用于二维和三维图像，计算分水岭变换并将结果标签矩阵显示为RGB图像。(The watershed algorithm is an image region segmentation method. In the segmentation process, it will use the similarity with neighboring pixels as an important reference basis, so as to connect pixels that are close in space and have similar gray values to each other. Forming a closed contour, closedness is an important feature of the watershed algorithm. This example applies to 2D and 3D images, calculates the watershed transformation and displays the resulting label matrix as an RGB image.)

可以用于图像的canny滤波处理，直接输入图像，可以保存下来处理后的canny图像(It can be used for canny filtering of images, directly inputting images, and preserving processed canny images.)

去除彩色图像中的阴影，以便于图像分割，物体识别以及跟踪(To remove the shadow of color images in order to facilitate the image segmentation, object recognition and tracking)

用matlab实现了一系列示例：球差，慧差，像差，像散。(Using matlab to achieve a series of examples: spherical aberration, coma, aberration, astigmatism.)

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.)