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I'm studying OpenCV and, in computer vision and image processing, people speak of blobs, contours, connected regions, and I sometimes hear the phrase "image moments".
I know of an article on Wikipedia about it, but I think it is too technical. I don't really want to go deep into the math background but I want to know what I'm talking about.
Could someone explain to me what image moments are in plain English?
Image moments are the same notion as in Mechanics. The first order moment will give you the center of mass, where the mass of a pixel is its intensity, second order moment will tell you how this mass varies around the center of mass, etc. In the same way as you obtain a frame of inertia for a real world object, you can obtain one from the image moments. That will give you the principal axes of the shape you want to describe.
An image moment is simply a number that characterizes the image, construed as the realization of a spatial random variable. If you've taken any probability class, you should remember the concepts of mean and variance, which are derived from the first and second moments of the random variable (the n'th moment of an r.v. is the expectation of its n'th power). Furthermore, the moments of a random variable collectively prescribe its distribution. In other words, you can reduce a probability distribution to a sequence of numbers, and this is useful when you want to compare distributions numerically.
In image processing, if you want to compare images, you might not want the comparison to be sensitive to minor things like rotation, translation, and scale (since the image remains fundamentally the same). Thus the motivation behind invariant moments you see in the Wikipedia article you cited.
