matlab识别边缘小程序

KL变换的Matlab实现 KL 变换是图像正交变换的一种，在图像压缩领域有着广泛应用。(KL Transform Matlab realize KL transform is a kind of orthogonal transformation image in the field of image compression have broad application.)

影像融合评价程序，计算标准偏差，平均梯度，相关系数，光谱扭曲度等融合指标。(Image fusion evaluation, calculation of standard deviation, average gradient, correlation coefficient, spectral distortions, such as integration targets.)

说明：  机器学习专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技 能，为包括人脸识别领域的人工智能的发展做出了极大的贡献。本文探索如何应用机器学习中的一些技术，使计算机更好地完成人脸识别领域中的人脸检测和人脸验证。 在人脸检测方面，针对如何快速、准确地检出人脸的问题，基于DLIB中特征模型，可以快速提取检测出人脸，并且提取出人脸特征点,主要作用就是快速检测定位人脸。在训练过程中，引入了ResNet机器学习算法，该算法采用多层卷积神经网络结构，对人脸进行多层特征提取和描述，得到人脸特征描述符。通过测试可以证实了上述方法能够检测和识别人脸。(In terms of face detection, for the problem of how to detect faces quickly and accurately, based on the feature model in DLIB, you can quickly extract and detect faces, and extract face feature points, the main role is to quickly detect and locate faces. In the training process, the VGG-FACE machine learning algorithm is introduced. This algorithm uses a multi-layer convolutional neural network structure to perform multi-layer feature extraction and description on the face to obtain the face feature descriptor. Tests can confirm that the above method can detect and recognize human faces.)

进行基于绝缘子边缘检测与提取,能识别绝缘子裂纹的故障,通用性蛮好(Insulator-based edge detection and extraction, to identify cracks insulator failure, versatility fine)

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.)

Read jpeg rgb color structure

这是一个阈值分割算法，主要用来对图像进行二值化，提取感兴趣区域~~(This is a threshold segmentation algorithm, mainly used for binary images, extract region of interest ~ ~ )

目前电子稳像技术应用最为广泛的算法，包括特征点的提取和运动补偿。(Electronic image stabilization technology currently the most widely used algorithms, including the feature point extraction and motion compensation.)

浏览图片的缩略图时，点击某张缩略图，被点击的缩略图会在当前视图放大并且全屏显示，背景用黑色透明视图遮挡。再次点击图片，将回到缩略图浏览状态(Media Focus Manager)

绘制矢量水听器的心形指向性，方便初学者使用(Drawing the heart directivity of a vector hydrophone,this thing is convenient for beginners to use)