Image Segmentation using Python OpenCV

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# IMAGE-SEGMENTATION
There are various methods of image segmentation such as Thesholding method, Edge Based Method, Region Based method, Clustering Method, and many more.
In this project, I have done segmentation of images using Gaussian Mixture Model (GMM)

# Gaussian mixture models (GMM)

- GMM is based on the assumption that all data points come from a fine mixture of Gaussian distributions with unknown parameters.
- This is useful for modeling more complex data, that has multiple peaks. Sometimes one bell curve isn't enough. We can optimize this model for clustering so that we can classify the data into the discovered classes using the Expectation Maximization algorithm.
- Once we learn the Gaussian parameters, we can generate data from the same distribution as the source.
#
Suppose there are a set of data points that needs to be grouped into several parts or clusters based on their similarity. In machine learning, this is known as clustering.
- Mathematical calculation and formulation for GGM have been skipped here.

# Code Dependencies
- Numpy
- Matplotlib
- cv2
- sklearn.mixture

code: https://github.com/noorkhokhar99/IMAGE-SEGMENTATION-GMM

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