graphcuts

GMSD 基于图像特征的质量评价，matlab编写语言(GMSD image assessment)

本实例实现了以下功能： 　　1.39条码生成功能 　　2.EAN-13条码生成功能 　　3.Code128条码生成功能 　　4.QR Code码制的二维条码生成 　　5.二维码制的解析功能    

说明：  用optisystem制作得FBG 滤波器(FBG filter was made with optisystem)

该源代码包含图像10个不变矩的图像检索，小波模极大值法边缘检测，结合颜色特征与不变矩的图像检索源程序(The source code includes images of invariant moments 10 image retrieval, wavelet modulus maximum edge detection method, combining color characteristics and the moment invariants image retrieval source program)

Jsteg算法实现，Jsteg是一种基于JPEG的常用信息隐藏算法步骤如下: 首先，把掩体图像分为不重叠的8*8的子块，对每一子块进行DCT并对变换得到的DCT系数进行量化 其次，将待隐藏的信息进行加密，将加密结果嵌入到量化后值不为0，1或-1的DCT系数的最 低有效位(LSB)中，其嵌入顺序是按zigzag扫描顺序进行的。最后，用JPEG的嫡编码(包括哈夫曼编码，游程编码及DPCM)对嵌入秘密信息后的每一子块进行编码，从而得到一个含有秘密信息的JPEG stego文件。(Jsteg Algorithm)

从一个模式识别toolbox中摘取的计算zernike不变矩的源代码，结果为图像的n阶m重zernike不变矩(From a pattern recognition toolbox extracted Zernike moment invariants calculation of the source code, the results of image re-n-order m Zernike moment invariants)

几个大牛对集成了国内外特征提取最前沿的算法，特征提取，DOG,HARRIS,HARRIS-LAPLACE,HARRIS-AFFINE,MSER.描述符，SIFT,AFFINE-SIFT,GLOH.经测试，速度快，描述符准确。(Integration of feature extraction algorithms from several expert of domestic and international, feature extraction： DOG, HARRIS, HARRIS-LAPLACE, HARRIS-AFFINE, MSER； Descriptors： SIFT, AFFINE-SIFT, GLOH. The test reveal the speed and accurate descriptor.)

KMeans图像聚类分割：用K-Means聚类方法来对图像进行分割。主要是对图像的颜色进行聚类。开发环境：VC6，需要安装OpenCV。(KMeans clustering in image segmentation: with K-Means clustering approach to image segmentation. Mainly the color of the image cluster. Development Environment: VC6, need to install OpenCV.)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )

卡夫曼算法，目标跟踪，可预测。简单算法，我在后面增加了汉语注释，方便初学者了解。(Target tracking using kalman，using this matlab program, you can see a ball tracked with a lable.)