superpixels.tar

使用K均值做聚类分析来进行图像分割,把原始图像转换到HSV空间做聚类分析(use k-means clustering to segment the image)

说明：  基于Halcon开发的掩模 纯手工编辑(Mask editing based on Halcon)

说明：  均值比差异图生成，用于SAR图像变化检测，生成差异图(mean ratio difference map generation, used for SAR image change detection, generate difference map)

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 )

网上下的CLAHE的matlab程序，原先运行有点问题，我修改了下，保证可以通畅运行了。(CLAHE algorithm for image enhancemnt)

利用基于直方图的自适应阈值方法实现分割前景与背景(The use of foreground and background to achieve segmentation adaptive threshold method based on histogram)

图片极坐标转换,可实现极坐标转换，以及求傅里叶变换(Picture polar coordinate conversion, convert polar coordinates can be realized, as well as seeking the Fourier transform)

说明：  360°图像序列的柱面全景拼接算法，matlab仿真程序。(360 image sequence cylindrical panoramic mosaic algorithm, matlab simulation program.)

褶积模型 正演模拟 子波与反射系数合成地震响应(model zhengyanmoni zhejimoxing)

极化SAR图像Four component 目标分解结果，使用数据为Flevoland地区部分数据，可用于极化SAR图像分类(Four component decomposition result of PolSAR image, the used dataset is the subarea of Flevoland region, it can be used as the classification features for PolSAR image classification.)