GUI

利用稀疏表示进行图像去噪时，需要生成两种学习字典，该源码比较了生成的两种字典的优劣(Denoising sparse representation, it is necessary to generate two learning dictionary, which compares the generated source code of the merits of two dictionary)

brovery图像融合代码，效果比较好，还有两幅测试图像，一幅是高分辨率的全彩色图像，另一幅是多光谱图像(brovery image fusion code, the results were better. There are two test images, a high-resolution full-color images, the other is a multi-spectral image)

k均值图像分割，经典的图像分割方法，算法简单，效果好。(k-means image segmentation)

说明：  图像拼接的Harris 算法，精确有效，实现图像无缝拼接，多幅图片显示，谢谢，希望得到采纳(image mosaicHarris algorithm for image mosaic, accurate and effective, seamless image mosaic)

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 )

加权核范数最小化应用于图像处理的代码，很实用，且是图像处理领域很新的方法源码，所以该方法效果很好，希望对大家有用。(Nuclear weighted norm minimization codes used in image processing, it is useful, and it is a new image processing method for the source, so that this method works well, want to be useful.)

我改写后的graphbrian类，原本支持柱状图饼图和折线图的，现在添加了对这些图的风格调整，主要是增加了调整绘图区背景色和文本背景的功能，还增加了把图片保存为bmp文件的功能，以便用其他软件方便的打印。(I would then graphbrian category, which originally supported the pie chart histogram and line graph, and now added to adjust the style of these plans, the main adjustment is to increase the drawing area background color and text background function, but also an increase of the picture saved as bmp files, in order to use other software to facilitate printing.)

这是一个matlab tld算法，供大家参考(This is a matlab tld algorithm, for your reference)

说明：  比较硬阈值、软阈值法图像去噪，并提出折中阈值法去噪(Comparison of hard thresholding, soft thresholding image denoising, and put forward a compromise threshold denoising)

对脉搏波的波峰波谷以及重搏波拐点的检测，挺好用的。。(On the pulse wave dicrotic wave peaks and valleys as well as the inflection point detection, very good use. .)