RANSAC_plane

用Matlab实现基于核函数的C均值聚类图像分割，实验好，好用(Using Matlab implementation of kernel-based C-means clustering image segmentation, experimental is good, easy to use)

利用模糊C均值图像分割,此为matlab环境下的仿真代码.(Using fuzzy C-means image segmentation, this matlab code simulation environment.)

KLDIV Kullback-Leibler or Jensen-Shannon divergence between two distributions.

说明：  分数阶pi控制的直流电机调速系统，速度环是分数阶的，转速环是整数阶的。(DC motor speed control system with fractional order PI control)

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.)

说明：  计算机图形学初级学者（教科书画法）中点画圆算法(computer graphics beginner (textbook method), the algrithm of middle point circle-drawing.)

对超声图像的去噪和分割，可以显示肿瘤图像的边缘和面积(For ultrasound image denoising and segmentation, image can show the tumor edge and area)

针对电气设备同一场景的红外与可见光图像间一致特征难以提取和匹配的问题，提出了一种基于斜率一致性的配准方法。首先通过数学形态学方法分别提取红外与可见光图像的边缘，得到粗边缘图像；然后通过SURF算法提取两幅边缘图像的特征点，根据正确的匹配点对之间斜率一致性的先验知识，进行特征点匹配；最后通过最小二乘法求得仿射变换模型参数并实现两幅图像的配准。实验结果表明，该方法有效提高了匹配点对的正确率，能够对电气设备红外和可见光图像实现高精度的配准。(In this paper, a method based on slope consistency is proposed to solve the problem that the uniform features of infrared and visible images are difficult to extract and match. Firstly, the edges of infrared and visible images were extracted by mathematical morphology, and the rough edge images were obtained. Then, the feature points of two edge images were extracted by SURF algorithm, and the priori knowledge of slope consistency was obtained according to the correct matching point, and the feature points were matched. Finally, the model parameters of affine transformation are obtained by least square method and the registration of two images is realized. The experimental results show that this method can effectively improve the accuracy of matching points, and can realize high precision registration of infrared and visible images of electrical equipment.)

在matlab中的分形图像放大程序；对初学者很有用哦！(In the matlab source code to raise the image resolution is indeed very useful!)

基于混沌置乱的DCT域灰度级盲水印算法 需要的可以看下~(Based on Chaotic Scrambling DCT-domain blind watermarking algorithm for gray-level needs can facie ~)