nlms_filter

一阶扩展卡尔曼滤波，二阶扩展卡尔曼滤波，和Rauch - tu - Striebel平滑器(Perform Extended Kalman Filter prediction step. Extended Kalman Filter measurement update step. Extended Rauch-Tung-Striebel smoother algorithm. Calculate "smoothed" sequence from given Kalman filter output sequence by conditioning all steps to all measurements.)

在本演示中，一个简单的图像检索方法，提出基于图像的颜色分布。用户只需提供一个“例子”的形象和搜索是基于这个例子（例如基于查询的图像）。包括图像和教学指令!!!(In this demo, a simple image retrieval method is presented, based on the color distribution of the images. The user simply provides an "example" image and the search is based upon that example (query by image example). Experiment image and instruction included!!!)

muller法, 可以對方程式求數值解 muller method and newton method(muller method)

说明：  图像处理里面的 椒盐分布 matlab里面图像处理很常用的 (Salt-pepper distribution)

说明：  利用最小二乘法对实验数据进行曲线拟合，可得到比较准确的拟合图形以及干涉代码。(The least square method is used to perform curve fitting on the experimental data to obtain a more accurate fitting graph.)

matlab 程序 用于成学matlab的人 很好的(matlab program for matlab people into learning good)

说明：  生物特征识别技术作为一种身份鉴别手段具有独特的优势。在生物特征识别系统中, 采集数据的预处 理是不可或缺的环节。以人脸识别为例, 介绍了一种在复杂背景的彩色图像中, 基于颜色和知识的生物特征预处理 方法。首先利用颜色信息从图像中分割出类肤色区域, 然后运用人脸几何特征和眼睛的提取定位人脸。仿真实验表 明, 该算法能较为准确地定位彩色图像中的正面或小角度偏转的人脸, 具有较快的运算速度。(Biometrics as an identification tool has a unique advantage. In biometric systems, collect data preprocessing is an indispensable link. To face recognition, for example, presents a complex background in color images based on color and knowledge of the biological characteristics of pretreatment. The first to use color information to separate out the classes from the image color region, and then use face extraction of geometrical features and eye positioning face. Simulation results show that the algorithm can be more accurately locate color images in a positive or small-angle deflection of the face, with a faster computational speed.)

说明：  详细介绍了matlab图像处理函数，并总结了函数的适用情况。实验问题详细的答案讲解。(Details of the matlab image processing function, and summarizes the functions of the application. Detailed answers to explain the experimental problem.)

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returns the Jacobian for the position of an SPFA