RD

目前最好的空域图像隐写算法，亲测已跑通！(the best Steganography)

matlab回归法做图像变化检测。有图片有结果，有论文说明，可参照使用(Matlab regression method for image change detection. There are pictures of the results, there are papers, can refer to use)

说明：  提出了一种基于数学形态学的多尺度熵权边缘检测方法。信息熵为图像的内在特性，由它来决定不同结构元 素所提取的边缘在最终合成边缘中所占的比重，是自适应的和客观的(A multi-scale based on mathematical morphology edge detection method of entropy. Information entropy inherent characteristics of the image, from which different structural elements to determine the edge of the extracted edge in the final synthesis of proportion, is adaptive and objective)

在图像的DCT系数上嵌入信息，图像为常见的512*512大小的灰度图（实验所用图像为LENA.BMP）。嵌入的信息为一二进制Logo图（实验所用logo图为tj-logo.bmp），该图大小为32*32，共1024个Bit。 1. 在LENA图中嵌入水印，输出嵌入后的水印图像。 2. 对水印图像解码，输出解码后得到的logo图，并统计错误率。 3. 使用不同的嵌入强度进行嵌入，观察其对作品直觉效果和解码效果的影响，绘制相应的曲线。 4. 使用加入噪声、改变对比度、JPEG压缩等攻击方法对水印作品进行攻击，查看解码的效果。 (Information embedded in the DCT coefficients of the image, the image of the common 512* 512 size gray-scale (experimental image LENA.BMP). Information embedded in a binary Logo Figure (used in the experiment logo picture shows the tj-logo.bmp), the plot size 32* 32, 1024 Bit. LENA Figure embedded watermark, the watermark image output embedded. (2) logo watermark image decoding, the output decoded Figure statistical error rate. (3) using a different embedding strength for embedding, observe the works intuitive effects and decoding results, and draw the corresponding curve. (4) by adding the noise, change the contrast, JPEG compression attack methods to attack, to see the effect of decoding the watermark works.)

功能很全的车牌识别系统，图像与处理，车牌定位，字符分割和字符识别等(Full license plate recognition system, image processing, license plate location, character segmentation and character recognition.)

图像去噪，去除图像的噪点 图像去噪，去除图像的噪点(Image Denoising)

CCD 摄像机内外部参数快速准确的标定方法(Fast and accurate calibration method for internal and external parameters of CCD camera)

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 )

分别用一阶、二阶以及Canny边缘检测算法进行图像边缘检测(Respectively first, second, and Canny edge detection algorithm for image edge detection)

matlab code for image fusion based on curvelet transform and genetic algorithm