MODEL4

本源码是基于Markov chain Monte Carlo (MCMC)的Bayesian inference工具包,其中包括MCMC采样，基于MCMC的高斯分类，同时描述了采样的一些方法。其中还有使用文档(toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods)

做完边缘提取之后，有的边缘断断续续，需要进行边缘连接，将其连成完整线。(Done after edge detection, and some off the edge, the edge needed to connect, to link into the full line.)

应用harris算子进行角点检测，然后利用相关性检测得到匹配焦点，然后进行图像的重采样，适于对于彩色图像的处理，对于灰度图像，只要将harris算子检测时的图像从彩色道灰度图像的语句去掉即可(Application of operator harris corner detection, and then make use of relevant test match to be the focus, and then carried out resampling images, suitable for color image processing, for gray-scale images, as long as the operator will harris Detect when the image from color Road gray-scale images can be removed from the statement)

改程序就代码可以找出从不同角度或区间的两幅遥感影像的同名点(Change the program code can find it from different angles of the same name point or interval of two remote sensing images)

说明：  二维emd的MATLAB代码，需要下载rbf工具箱，亲测可用(Two-dimensional EMD MATLAB code, need to download the RBF toolbox, pro-test available)

bemd分解得到图像基本imf，二维希尔伯特变换(Image basic IMF obtained by BEMD decomposition.Two-dimensional Hilbert transform)

经典的聚束SAR成像算法-极坐标格式成像算法(A typical imaging algorithm of spotlight mode SAR)

图像处理模式识别一种分类算法：svm，对于所提取的图像的特征进行训练学习然后分类。(Image processing, pattern recognition of a classification algorithm: SVM trained to learn the characteristics of the extracted image and then classified.)

基于Bandlet小波的图像去噪研究，具有较好的效果。(Bandlet wavelet-based image denoising, with good results.)

实现一个高精度指针仪表读数自动识别系统．用数码摄像头采集仪表的图像，然后利用数字图像处理技术识别刻度和指针，根据指针和刻度的相对位置计算指针读数值．使用条件霍夫变换（Constrained HOUGH Transfer）和中心投影分析法实现刻度的全自动识别，采用快速中心投影法进行指针检测与识别，识别速度达到68ms．实验结果表明该方法比传统方法速度快、精度高，系统读数平均误差仅为0．016％(To achieve a high precision automatic identification system pointer meter reading. Instrument with a digital camera capture images, then using digital image processing technology to identify scale and pointer, pointer and scale according to the relative position of the pointer readings calculated. Use Hough transform (Constrained HOUGH Transfer) and center projection analysis method to achieve scale automatic identification method using fast center projection target detection and recognition, recognition speed of 68ms. Experimental results show that faster than traditional methods, high accuracy, the system average error of only 0.016 of reading)