Pattern matching vs probabilities
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Hi,
I would like to ask if it can be assumed that the result of a pattern matching technique represents a probability density function.
To be a little bit more specific let's assume that I have a function f(x,y) with x,y having a specific value, which is being compared with the same function f(x,y) for all possible x,y. The resulting function is let's say g(x,y). Is it possible to assume that the g(x,y) represents a probability density function so that for a specific x,y it gives me the probability that x=x0 and y=y0?
Thanks in advance
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Answers (2)
Image Analyst
on 21 May 2014
No, I would not say that. For example normalized cross correlation, like the demo I've attached, is a kind of pattern recognition. See the image below.
But I wouldn't say that the image in the lower left is a kind of PDF.
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Image Analyst
on 21 May 2014
Regarding your answer. You can see from the picture that the garlic looks a lot like the onion but the response was not all that high. In fact the similarity of the garlic is about the same as other vegetables even ones of different colors. Maybe that's what is wanted, but maybe not. There is a field called CBIR (Content Based Image Retrieval) that can be used to identify such similar-but-not-exact regions. For example it can find all images that have babies or buildings in them. That's out of my area of expertise. Maybe you might want to look into cbir.
For the case you gave in your answer, I guess you could call the distribution of reported locations a PDF if you want to.
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