第6次课程序

说明：  TV算法的MATLAB版，绝对好用，已经经过本人测试过的(TV algorithm in MATLAB version, absolutely easy to)

卡尔曼滤波、无迹卡尔曼滤波、扩展卡尔曼滤波等滤波程序，本人收集了实验室所有这个方面的程序，欢迎大家下载，非常好用~(Kalman filtering, unscented Kalman filter, extended Kalman filtering, filtering program, I collected all the aspects of laboratory procedures, are welcome to download and very easy to use ~)

说明：  MATLAB实现基于互相关的亚像素级图像配准源代码,MATLAB_Image_subpixel_Mutual_info(MATLAB-based cross-correlation of sub-pixel image registration source code, MATLAB_Image_subpixel_Mutual_info)

mean shift图像分割软件,只能对灰度图像进行处理,如果对彩色图像进行处理,需要转换(mean shift image segmentation software, only for grayscale image processing, if the right color image processing, needs to be converted)

MAD相似性度量准则的改进，实现模板匹配程序(MAD similarity measurement criteria to improve and achieve template matching procedures)

说明：  天线方向图，贝叶斯加权，阵元不加权子阵不加权时的方向图(antenna pattern,beyliss ,array,Antenna pattern, Bayes weighted, the pattern of the unweighted sub-array of the array element)

图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )

使用说明 使用时打开此例题目录下pic中的图片，然后依次单击按钮“转”、“1”、“2”、“3”、“4”和“5”，就可以实现精确的车牌定位。 具体步骤 1.24位真彩色->256色灰度图。 2.预处理:中值滤波。 3.二值化:用一个初始阈值T对图像A进行二值化得到二值化图像B。 初始阈值T的确定方法是:选择阈值T=Gmax-(Gmax-Gmin)/3,Gmax和Gmin分别是最高、最低灰度值。 该阈值对不同牌照有一定的适应性,能够保证背景基本被置为0,以突出牌照区域。 4.削弱背景干扰。对图像B做简单的相邻像素灰度值相减,得到新的图像G,即Gi,j=|Pi,j-Pi,j-1|i=0,1,…,439 j=0,1,…,639Gi,0=Pi,0,左边缘直接赋值,不会影响整体效果。 5.用自定义模板进行中值滤波 区域灰度基本被赋值为0。考虑到文字是由许多短竖线组成,而背景噪声有一大部分是孤立噪声,用模板(1,1,1,1,1)T对G进行中值滤波,能够得到除掉了大部分干扰的图像C。 6.牌照搜索:利用水平投影法检测车牌水平位置,利用垂直投影法检测车牌垂直位置。 7.区域裁剪,截取车牌图像。()

SURF算法为SIFT算法的改进，运算速度得到了很大的提高。本资料为基于MATLAB的SURF算法实现，对图像匹配的研究方向会有一定帮助。(SURF algorithm SIFT algorithm, computing speed has been greatly improved. MATLAB based on the information of the SURF algorithm for image matching the direction of the research will certainly help.)

实现粘连分割，对有粘连的物体分割效果很好，使用了局部极值结合分水岭算法(Achieve adhesion segmentation, to have adhesion object segmentation effect is very good, the use of the local extremum combined with watershed algorithm )