资源列表
Handwritten_numeral_recognition_of_the_template_ma
- 手写汉字识别，本文采用模板匹配的方法，进行识别，模板匹配方法的通用性大家都很了解，你可以在本代码的基础上改善新的功能-Handwritten Chinese character recognition, this paper adopts the template matching method, identification, template matching method versatility are all too familiar, you can code on the basis
Thinning_Algorithm
- 细化算法，无论是文字处理还是图像识别，如掌纹识别，指纹识别，都可以用此程序进行改进，为你的工作或研究带来方便-Thinning algorithm, whether word processing or image recognition, such as palmprint recognition, fingerprint recognition can be improved with this procedure, for your work or research convenient
ImagepatternrecognitiontechnologytoachievetheVC.ra
- 图像模式识别之VC＋＋技术实现，里面包含模式识别中的各种算法的实现-Image pattern recognition technology to achieve the VC++, which contains a variety of pattern recognition algorithms to achieve
yalefaces
- Yale人脸库（美国）： 耶鲁大学，15人，每人11张照片，主要包括光照条件的变化，表情的变化等。-Yale Face Database (U.S.): Yale University, 15 people, each 11 photos, mainly including changes in lighting conditions, expressions of the change.
Filebatch
- 本程序主要实现了新建文件、复制、移动、删除、解压缩及分割/合并文件等功能。-This program achieved a major new file, copy, move, delete, extract and split/merge files and other functions.
icaFaces
- ICA编写的一个程序，用于人脸识别。希望对大家有作用。-ICA written a program for face recognition. We want to play a role.
luokuo
- 轮廓跟踪Visual+C++源代码，欢迎下载，谢谢大家-Contour Tracking Visual+ C++ source code, welcome to download, thank you
zifushibie
- 用特征匹配做的字符识别，可以先进行样本的训练，再进行识别-Feature matching to do with character recognition, the training samples can first be done, and then identify
CombinedBlurandAffineInvariants
- 計算Affine Invariants的值,I1I6-Affine Invariants calculated values, I1I6
ganzhiqi
- 模式识别里的感知器算法，基于分类应用，校正方法是最优化技术中的梯度下降法。-Inside the sensor pattern recognition algorithm, based on classification applications, calibration method is the optimization of the gradient descent.
C_means_zw
- 模式识别领域里的聚类分析：C-MEANS算法-Cluster analysis: C-MEANS ALGORITHM
oushijulifu
- 现代模式识别根据相似阈值和最小距离原则的简单聚类方法-Modern pattern recognition based on the similarity threshold values and the principles of a simple minimum distance clustering method