ti_5_1

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)

说明：  使用薄板样条函数，完成点对的非刚体配准方法。此类提供算法的封装。(Use of Thin plate spline, the completion point of the non-rigid registration method. Such packages provide the algorithm.-)

学习MATLAB必须学会的一些简单示例，给初学者提供较好的学习过程。减少书写代码的时间(MATLAB must learn to learn some simple example to provide beginners a better learning process. Reduce the time writing code)

Contrast Enhancement Utilities (Image Equalization)

vim配置文件，可以直接解压后放在/home/username(vim config file,u can put this file inthe path /home/usrname)

EBMA and HBMA using MATLAB

利用Tikhonov正则化实现的超分辨率图像序列的重建，效果还好(Use Tikhonov regularization to achieve super-resolution image sequence reconstruction, better results)

自己编写的kalman滤波例子，实现对X,y坐标跟踪的例子，欢迎大家参考提意见(themselves prepared Kalman filtering example, the realization of X, y coordinates tracking example, Welcome reference comment)

基于MATLAB的模式识别的参数估计的最大似然估计，处理水环境分类问题，共分为5类，最后利用贝叶斯决策得出五类总的识别率。 (Maximum likelihood estimation of parameters of pattern recognition of MATLAB estimation based on classification of water environment treatment problem, is divided into 5 categories, the last use of Bayesian decision obtained five types of the total recognition rate. )

蒙特卡洛算法的例程，包含所需的全部相关程序(Monte Carlo algorithm routine)