🚀 Welcome to Tutorial 205 in our OpenCV & C++ Series!
In this video, we explore Expectation Maximization (EM), a powerful unsupervised machine learning algorithm used for clustering and density estimation. 🔄✨
This video shows the effect of parameter changes on the result. The parameters we examine in the video are: cluster number, termination criteria, and covariance matrix type.
🌠 EM class: https://docs.opencv.org/4.8.0/d1/dfb/classcv_1_1ml_1_1EM.html
🌠 Tutorial Series Plan: https://docs.opencv.org/4.8.0/modules.html
🌠 Stackoverflow: https://stackoverflow.com/users/11048887/yunus-temurlenk?tab=profile
🌠 Github: https://github.com/yunus-temurlenk?tab=repositories
🌠 Twitter: https://twitter.com/code_enjoy
🌠 Hashnode: https://yunustemurlenk.hashnode.dev/
▬ Contents of this video ▬▬▬▬▬▬▬▬▬▬
0:00 - Introduction
0:24 - Coding
If you see any mistake or have advice, please comment. Thanks for watching…
#OpenCV #Cplusplus #MachineLearning #EM #ExpectationMaximization #ComputerVision
