Joint probability distribution of two images

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SUMMARY

The discussion focuses on the joint probability distribution of two images, specifically how to calculate it using pixel values from images A and B. The conventional expression for this distribution is denoted as pA,B(a, b), which is derived by counting the occurrences of pixel values a and b at corresponding positions in the images and dividing by the total number of pixels N. The calculations provided illustrate the joint probabilities for specific pixel values, confirming that the joint distribution reflects the relationship between pixel intensities in both images. The terminology used in the context of joint distributions is acknowledged as subjective, with various methods available for defining such distributions.

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atrus_ovis
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I'm posting this here, as i feel it's more probability-related than image processing.

I'm reading this lecture pdf.
At end of page 1 , beginning of page 2 it says:
The spatial information obviously required for a registration method is provided by the
definition of a joint probability distribution that depends simultaneously on image A and B.

The conventional expression for it is pA,B (a, b). It is calculated as the number of times out
of the total number of pixels N that a pixel in A contains the value a and the same pixel
that is, the pixel in the same image position, in B contains the value b; this number of pixels
is then divided by the total number of pixels to give the joint probability of a, b.

Is this a bit vague, or am i missing something?
Since we are calculating frequency of the value a in the pixels of image A, when was the information about any position introduced?

Or does it mean that for *any* pixel in image A that contains the value a, check if the pixel(s) in image B at the corresponding position have the value b?

If this is too specific or too dependent on image processing,can you provide some material to build my intuition about joint probability distributions?

Thank you.
 
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Let's say the images are
A = \begin{pmatrix} 1&1& 2 \\ 2&7&1 \end{pmatrix}
and
B = \begin{pmatrix} 1&2&2 \\ 2&1&2 \end{pmatrix}

My interpretation of that passage is that

p_{A,B}(1,1) = \frac{1}{6}
p_{A,B}(1,2) = \frac{2}{6}
p_{A,B}(1,7) = \frac{0}{6}

p_{A,B}(2,1) = \frac{0}{6}
p_{A,B}(2,2) = \frac{2}{6}
p_{A,B}(2,7) = \frac{0}{6}

p_{A,B}(7,1) = \frac{1}{6}
p_{A,B}(7,2) = \frac{0}{6}
p_{A,B}(7,7) = \frac{0}{6}

Although there are (3)(3) = 9 possible ordered pairs of pixel values to consider, the total match-ups will only be 6.

As to whether we should call this "the" joint distribution of two images - that is merely the terminology that those authors wish to use. There are many many ways to take properties of images and define joint distributions.
 

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