Sparse-Representation-

说明：  基于opencv的支持向量基（SVM）的代码，利用SVM解决2维空间向量的3级分类问题。(Opencv-based support vector machine (SVM) of the code, the use of SVM to solve 2-dimensional vector space of three classifications.)

Relief-F 算法是由 Kononenko 在 Relief 算法基础上扩展得到的，可以用来解决多分类与回归问题。 Relief-F 特征选择算法利用特征与类别间的相关程度判别特征的敏感程度，敏感的特征能使同类样本靠近，非同类样本远离。基本思想是： 先随机从总样本集 D中获取一个样本 R， 然后在 与 R 同类样本集中和与R 异类 样 本 集 中 各 获 取 k 个近 邻 样 本， 在 特 征 集 F中如果某个特征上异类样本的距离大于同类样本，则这个特征是有利于分类的，增加其权重，重复 n 次 取所有结果的均值作为每个特征的最终权重值.

说明：  harris角点检测算法，用于图像拼接的配准(harris corner detection algorithm for image mosaic of the registration)

基于OPENCV的SIFT特征提取与匹配算法。包含完整的从图像高斯金字塔、DOG、空间极值点提取、关键点描述、KDtree匹配等关键步骤的全部函数实现，对全面深入理解Lowe的SIFT算法有莫大帮助。程序运行前须安装（1）OpenCV: http://opencvlibrary.sourceforge.net （2）SIFT: http://web.engr.oregonstate.edu/~hess/index.html，并配置其环境参数。(OPENCV the SIFT-based feature extraction and matching algorithm. Contains a complete Gaussian pyramid from the image, DOG, space extremum point extraction, description of key points, KDtree matching key step in the realization of the full function of the comprehensive and in-depth understanding of Lowe s SIFT algorithm of tremendous help. Program to run before the installation of (1) OpenCV: http://opencvlibrary.sourceforge.net (2) SIFT: http://web.engr.oregonstate.edu/ ~ hess/index.html, and configure the parameters of their environment.)

1.能够对简单瑕疵点进行检测：正常线、断线、重线、跳线、平纹面料、平纹面料疵点、水印疵点、染色疵点 能够对不同的纹理布匹进行检测：平纹、斜纹等(翻译 1. Be able to perform simple point defect detection: the normal lines, broken, heavy lines, jumpers, plain weave, plain weave fabric faults, watermark defects, dyeing defects Able to detect different textures of cloth: plain weave, twill)

基于学习单幅图像超分辨率重建算法源代码。(based on the singal picture super resolution founding)

可实现答题卡阅卷系统中的图像倾斜矫正，采用Hough进行直线检测，并根据所定位的特征直线位置计算倾斜角度，再进行图像旋转，得到校正结果(chieve answer sheet scoring system image tilt correction, performed using Hough line detection, and calculates the inclination angle in accordance with the target characteristic linear position, then rotate the image, corrected results)

说明：  该课题为基于MATLAB的汽车出入库识别系统，带有丰富的人机交互GUI界面。目前毕业设计选题中，传统的中规中矩的车牌识别不易得到高分，必须要在此基础上有所创新方得可以避开其他雷同课题，不会轻易被导师被否决而导致毕设不过。因此建议在车牌识别基础上加入多种方法的对比，看看哪种方法的精度高。虽然目前有很多方法的车牌识别，但是都是在各自的测试库里面测试的，比如测试的车牌图像不同，字符库不同，导致无法做对比。整个设计在一个GUI界面上完成。(This project is based on MATLAB vehicle in and out of the warehouse identification system, with rich human-computer interaction GUI interface. At present, in the topic selection of graduation project, the traditional regular license plate recognition is not easy to get high scores, so it is necessary to innovate on this basis to avoid other similar topics, and it will not be rejected easily by the tutor, leading to the failure of graduation project. Therefore, it is suggested to add a variety of methods on the basis of license plate recognition to see which method has high accuracy. Although there are many methods of license plate recognition, they are tested in their own test library, for example, the test license plate image is different, the character library is different, so it can not be compared. The whole design is completed on a GUI interface.)

一维中值滤波方法去噪和二维中值滤波方法去噪！(denoise by median，can remove noise of 1D&2D！)

关于阈值处理的一部分参考文献，对于图像的二值化等很有帮助，是经过精心选择的参考文件。(References on the part of the processing threshold for image binarization helpful, is carefully selected reference files.)