资源列表
faceface
- 系统有以下部分组成：电脑自带摄像头拍照、人脸检测、将人脸照片录入数据库、输入照片进行人脸识别。本程序是基于肤色识别的方法对人脸进行检测，人脸肤色范围是100≤B≤120，140≤R≤160，所以将此范围内的像素点置白，剩余部分置黑。利用imerode函数对图片进行球状腐蚀，然后再对图片进行中值滤波，达到平滑效果。最后，对于这张已经缩放而且二值化和各种处理之后的照片来说，如果白化区域的像素点少于1000，就舍弃。将图片进行分割，这里我们引进了欧拉数。 这样就可以把一些类似颜色人脸的背景排除。
matlab_Bayes
- matlab基于贝叶斯的手写输入数字识别代码-matlab based on Bayesian handwritten input digital identification code
SVM-KMExample
- 林智仁教授最新开发的libSVM工具箱的源码及程序注释，很详细的程序注释，对于学习SVM的人有很大的帮助-Professor Lin Zhiren newly developed libSVM of toolbox source code and program notes, a very detailed program notes, a great help for people learning SVM
matlabOCR
- matlab 文字识别 经典代码 文字分割 文字识别-Matlab character recognition classic code
GeoMatch
- 模板匹配。根据模板和图像，得到匹配度，旋转度，所耗时间。 可基于此做很多开发-Template matching. According to the template and image matching, rotation, and the time spent. Do a lot of development can be based on
renlianshibie
- Gabor小波人脸识别，LBP特征提取，PCA，LPP降维。自己做的毕业设计。-Gabor wavelet face recognition, LBP feature extraction, PCA, LPP dimensionality reduction. Own graduation design.
GMKL
- 更加一般的多核学习算法，还附有算法的论文-More general multi-core learning algorithms, but also with algorithms papers
cPPPQRcode
- 修改了日本人写的QR码的编码程序,支持中文,采用UTF8编码，用快拍二维码和我查查二维码测试均未出现乱码。中文注释。-program for QR Encode.support for chinese.
LPR
- java车牌图像处理 输入一个车牌照片（不是整车的照片），识别出车牌上的字符-java image processing input a license plate photo (not the vehicle photo), to identify the license plate characters
CDBM-master
- 深度学习理论下的深度波尔兹曼机（DBM），用于图像的自动的特征提取及识别。-Depth Depth Boltzmann machine learning theory under (DBM), for automated image feature extraction and recognition.
shengfengzhengshibie
- 本程序包为基于CVI平台c语言开发的身份证识别应用，对身份证进行图像处理提取各部分信息块，再进行文字识别提取信息-This package is based on the identification card application CVI platform c language development of identity cards for image processing to extract information on the various parts of the block,
DeepLearnToolbox-master
- 这是关于深度学习的一些很重要的代码 包括基础的深度学习 RBM等，还有用深度学习去训练神经网络-This is about the depth of learning, including some very important code based on the depth of learning RBM, as well as by the depth of learning to train the neural network, etc.