ColorHistogramjiansuo

说明：  在 matlab中实现一种局部阈值化图像的算法(In matlab to achieve a local thresholding algorithm for image)

1、读取显示所给PET图像（SE2IM210）。 2、学习imhist函数，画出所给PET图像的直方图。 3、根据直方图为该PET图像选择合适的显示窗（imshow参数），使胸 部轮廓等解剖结构可见。 4、根据直方图选择合适阈值，分割肿瘤区域与正常区域，计算肿瘤区 域内图像灰度均值、方差、最大值及最小值。(1 give PET images displayed, read (SE2 IM210). 2, learning, imhist function, draw the give PET image histogram. 3, according to the histogram for the PET image to choose the appropriate display window (imshow parameters), make bosom Outline of anatomical structures such as visible. 4, according to the histogram, choose appropriate threshold segmentation tumor area and normal area, calculating the tumor area In the domain of image grayscale average and variance, the maximum and minimum values.)

实现图像的分块压缩感知重建，DCT变换，OMP重构算法(Image block compressed sensing reconstruction, DCT transform OMP reconstruction algorithm)

利用matlab对只包含RBG三个波段的图像进行植被提取，通过设定一定的阈值将植被与背景进行分割(The vegetation is extracted from the image containing only three bands of rbg by matlab, and the vegetation and background are segmented by setting a certain threshold.)

暗通道去雾算法，可将风景图片实现去污效果。不会影响其原有的分辨率。效果比传统的均衡化好。(Dark channel dehazing algorithm, can achieve the decontamination effect picture. Will not affect its original resolution. Results than traditional good equalization.)

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 )

fcm聚类分析,用距离方法进行聚类画图，有一定的可操作性(FCM clustering analysis, using distance method for clustering drawing, has a certain operability.)

Canny进行边缘检测，得到图像的边缘，然后sobel算子对图像边缘进行卷积，然后用Hough变换进行直线提取。(Canny edge detection to obtain the edge image, and then sobel edge operator for convolution, and then use the Hough transform for straight line extraction.)

最小二乘影像匹配，自己2012年8月写的程序，经过测试，内有详细注释，希望对大家有用。(Least Squares Matching write the program in August 2012, has been tested, with detailed notes, and hope to be useful.)

这是一个图像处理时用到的程序 可作为图像处理用非常有价值(This is an image processing procedures can be used as image processing with very valuable)