CRC

In this file you can find a simple but very effective algorithm for NN Search which I megalomaniacly called the GLTree.

说明：  贝叶斯建网程序，对于初学者有一定的帮助作用，希望能帮上大家的忙(NATALAB BAYES)

说明：  基于边缘导向的插值算法 首先估计出低分辨率图像的局部协方差系数，然后基于低分辨率图像的协方差和高分辨率图像的协方差之间的几何二元性，用估计出来的协方差进行插值得到高分辨率图像(Image interpolation based on oriented)

公共汽车悬挂系统模型的MATLAB仿真程序,完成悬挂控制系统的研究(bus suspension system model MATLAB simulation program, complete suspension control system research)

solves Laplace equation for a specific B.C. in 2-D on a disk.

利用smith圆图演示射频电路匹配过程，参数可调，界面良好。(Smith chart shows the use of RF matching process, adjustable parameters, the interface is good.)

细化骨架的简单程序，对二值图进行操作，如果对于空洞比较大的，先进行膨胀操作(Refinement of skeleton simple program, if the hole bigger, to expand operations )

粒子群算法求解多维约束函数极值，并与遗传算法比较。结果发现，粒子群有很好的精度。(A particle swarm optimization algorithm for solving the extreme value of multi dimensional constrained function, and compared with genetic algorithm. The results show that the particle swarm has a good accuracy.)

以3*3为模板在一幅灰度图像中寻找像素值大于给定阈值的所有点(To 3* 3 as the template to look at a gray image pixel value is greater than a given threshold for all points)

ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)