stereoscopic_vision

matlab追赶法实验报告完整版(matlab experiments to catch up with the full version of the report)

1 掌握不同的聚类方法—基于层次与基于划分的方法 2 学会层次聚类的单连接算法 3 学会K-means算法 (A master of different clustering methods- based on hierarchical partitioning method based on hierarchical clustering 2 Society 3 Society of single-link algorithm K-means algorithm)

建立了多径直扩信号模型，实现多径环境下直扩信号PN码的捕获(The multipath DS-SS signal model is built, and achieve the PN code acquisition under multipath. )

说明：  利用Matlab引擎、利用Matlab编译器、利用Matcom软件编译、调用Matlab数学函数库、及调用Matcom数学函数库。(Using Matlab engine, using Matlab compiler, use Matcom software build, call Matlab math library, and calls Matcom math library.)

基于MOGA方法的多目标遗传算法程序,本程序为通用包,可自行修改.(MOGA is based on the multi-objective Genetic Algorithms procedures, the procedures for the general packet may amend its own.)

Fractal dimension (FD) calculation using differential box counting algorithm.

逐行读取文本中的数据，自动存储到一个序列数组中(Text data read line by line, is automatically stored into an array sequence)

sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.(sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.)

说明：  利用matlab编写的小波分解分频和除躁程序，分为高低频率，并除去噪声，达到较好的效果。(Prepared using matlab wavelet decomposition impatient frequency and in addition to procedures, divided into high and low frequency, and to remove noise, to achieve good results.)

adaboost算法训练,tr_func_handle and te_func_handle are function handles for training and testing of a weak learner, respectively. The weak learner has to support the learning in weighted datasets. The prototypes of these functions has to be as follows.(adaboost train)