文件名称：C# Emgucv PCA
文件大小：295.13MB
文件格式：ZIP
更新时间：2020-05-16 12:45:19
PCA C# Emgucv
This article is designed to be the first in several to explain the use of the EMGU image processing wrapper. For more information on the EMGU wrapper please visit the EMGU website . If you are new to this wrapper see the Creating Your First EMGU Image Processing Project article. You may start with 3 warnings for the references not being found. Expand the References folder within the solution explorer delete the 3 with yellow warning icons and Add fresh references to them located within the Lib folder. If you have used this wrapper before please feel free to browse other examples on the EMGU Code Reference page Face Recognition has always been a popular subject for image processing and this article builds upon the work by Sergio Andrés Gutiérrez Rojas and his original article here[^]. The reason that face recognition is so popular is not only it’s real world application but also the common use of principal component analysis (PCA). PCA is an ideal method for recognizing statistical patterns in data. The popularity of face recognition is the fact a user can apply a method easily and see if it is working without needing to know to much about how the process is working. This article will look into PCA analysis and its application in more detail while discussing the use of parallel processing and the future of it in image analysis. The source code makes some key improvements over the original source both in usability and the way it trains and the use of parallel architecture for multiple face recognition.
【文件预览】：
Face Recognition 2.4.9
----Face Recognition.suo(56KB)
----Face Recognition()
--------Main Form1.resx(12KB)
--------Cascades()
--------Face Recognition.csproj(12KB)
--------Training Form.resx(11KB)
--------Main Form1.Designer.cs(17KB)
--------Program.cs(508B)
--------face.ico(4KB)
--------Properties()
--------Group_Photo_Unknowns.pdf(134KB)
--------x64()
--------x86()
--------Training Form.Designer.cs(10KB)
--------Lib()
--------obj()
--------Classifier_Train.cs(14KB)
--------bin()
--------Training Info()
--------Training Form.cs(14KB)
--------Main Form1.cs(14KB)
----Face Recognition.sln(1KB)