27220065

附带GUI界面，可用于各种图像的特征点提取，并对两幅图像进行配准拼接，使用的是sift算法( U9644 u5E26G0 u754C u972 u53EF u7528 u4E8E u5404 u79V U8FDB u884C u914D u51C6 u62FC u63A5 uFF0C u4F7F u7528 u7684 u662Fsift u7B97 u6CD5)

用atrous小波变换与EMD算法相结合的图像融合(Combination of image fusion with atrous wavelet transform and EMD algorithm)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

说明：  全变分去噪程序，用于图像去噪，该方法能克服图像去噪中的震荡效应，使图像更加光滑，并能保存边缘信息。(total variation denoisy)

Random ppm modulated analog signal under Complete class-based image processing, contains all of the source code, auto image, Correlation analysis process matlab method.

题目： 八宝粥的生产过程中需要用到大量的红豆（红蚕豆），而实际上从红豆产地购买得到的红豆中会参杂一些杂物和不合格红豆。为了保证八宝粥的质量，需要对红豆进行筛选，去除不合格红豆和其它杂物。目前国内在红豆筛选工序上仍然使用人工筛选的方式，存在人工成本高、精确度差、劳动强度大等缺点。因此八宝粥生产厂商迫切需求开发一款能够自动实现红豆筛选的自动化筛选机。针对上述需求，请设计筛选机中的基于可见光图像的红豆筛选算法，该算法应尽可能的准确的找到参杂在红豆原料中的不合格红豆和杂物。 附件包括两个不同实验环境下拍摄得到的红豆相关图片样本库，实验环境分别为自然光照条件和灯光条件。每个条件下的样本库包括合格、不合格、杂物和混合四类样本的图像，其中合格、不合格以及杂物的图像可用来训练或建模，混合类图像用来测试算法的准确性和实时性。同学可在自然光照环境下的样本库或者灯光条件下的样本库中任选其一，用于解决上述问题。 作业提交格式包括以下四个内容： 一、 问题分析 二、 算法设计 三、 实验结果(将自己选择的样本库中的混合样本图片中的样本个体进行分类，并给出可视化的分类结果。) (Title:Use a lot of red bean production process of eight-treasure porridge ( red bean ), but in fact from the Red areas available in red bean can blend some sundries and unqualified red bean. In order to ensure the quality of eight-treasure porridge, the need for screening of red bean, red bean and other debris removal is not qualified. At present in the domestic bean screening process still use manual screening method, has the disadvantages of high labor costs, poor accuracy, high labor intensity. Therefore, eight-treasure porridge manufacturers need to develop a can achieve automatic bean screening automatic screening machine. According to the above requirements, please design screening machine selection algorithm based on visible light image of the red, the algorithm should be as accurate as possible to find the mixed in the bean raw materials in the unqualified red bean and debris.Accessories include captured in two different experimental environment of the red bean image sample dat)

求解声压级，总声压，三分之一倍频程，倍频程，能量谱密度(get one third octave)

一个基于形态学图像细化的程序，处理效果挺不错的(Morphological image thinning based on the procedures)

大名鼎鼎的GN：genesis的相关开发资料 做液晶电视，以及OSD字符叠加以及高级视频控制的朋友可以来看看，这些资料可是要和GN签订保密协议才会给的！(Famous GN: genesis of the relevant information to do the development of LCD TV, as well as the OSD characters, as well as advanced video overlay control friends can look at it, but such information should be entered into confidentiality agreements and GN will give!)

对两幅图像提取特征点，并计算基本矩阵，对两幅图像进行极线矫正，并画出校正后图像特征点的极线，效果非常好(The two images of the image feature points in the feature points are extracted, and the calculated fundamental matrix, correction of lines of the two images, and after the draw correction lines, very good results)