Image correlation is kind of like matching faces to see if they look the same. Imagine you have two pictures of yourself - one from yesterday and one from today. You want to see if your face looks the same in both pictures. One way to do this is to look at the features in your face, like your eyes, nose, and mouth, and compare them.
This is similar to how image correlation works. It takes two images and compares them to see if they are similar or not. It does this by finding matching features in both images, like edges, lines, and shapes.
To do this, image correlation uses mathematical formulas that measure the similarity between two images. These formulas look at the values of the pixels in each image and compare them to each other. If the values are similar, then the images are considered to be correlated.
For example, imagine you have two images of circles. One image has a big circle in the middle, and the other has a small circle in the corner. If you use image correlation to compare these two images, it will find that they are not very correlated because the shapes are different. However, if you have two images of the same circle in the same position, image correlation will find a high correlation because the shapes are the same.
Image correlation is used for many things, like face recognition, tracking objects in videos, and medical imaging. It helps us understand how images are related to each other and how they change over time.