PFA_1

实现图像的相位相关匹配，该算法有一定的优点于其他算法相比。(images related to the phase matching, the algorithm has certain advantages compared to other algorithms.)

图像几何校正中的绝对变换使用matlab编程(Absolute transformation in image geometric correction using MATLAB programming)

"基于四元数卷积的彩色图像边缘检测"一文的matlab源代码，很有帮助(matlab source code of "Colour image edge detector based on quaternion convolution" Electronics Letters, 34(10), May 14 1998, 969-971)

应用于图像压缩的SPIHT算法代码，此代码的步骤很清晰，我自己加了很多注释，特别好理解。(SPIHT code which is used in image compression,with many notes of my own so really easy to understand.)

说明：  自己用python复现的NL-mean去噪，备注比较详细，便于理解原理； 之后会尝试复现更多图像领域的经典论文(The NL-mean reproduced by Python is used to denoise the noise. The notes are detailed and easy to understand the principle. We will try to reproduce more classical papers in the field of image.)

层析成像的数学方法与应用-一本关于层析成像的使用方法的详细讲解，对于初学者具有指导性意义。(Mathematical methods and applications of tomography- a detailed on the use of tomography methods to explain, for beginners with a guiding significance.)

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 )

画椭圆和圆和直线的二值图求边缘点斜率(Draw the ellipse and the circle and the straight line of the binary image to find the edge slopeDraw the ellipse and the circle and the straight line of the binary image to find the edge slope)

实现对一幅图像的汉字分割，利用图像点扫描(To achieve an image of the Chinese character segmentation, the use of the image spot scanning)

说明：  图像形态学处理，图像腐蚀，图像膨胀，图像细化，边缘检测(Morphological image processing, image corrosion, image expansion, image thinning, edge detection)