I will use a specific example from image processing to illustrate my question, but I'm actually interested in the higher level / abstract procedure. I must lack the specific vocabulary and my hunch is that my question relates to some topic in math / signal processing / computer science that I'm not yet aware of.
I extracted part of an image and I want to check if it contains an edge. Using some filtering technique (e.g. Sobel) I obtain pixels which are more likely to be part of an edge.
What I would like to obtain is the likelihood of the image to contain an edge. For example: The probability that this image contains an edge is p=0.82 .
Q1: Which processing steps am I missing here?
My intuitive approach is the following: Let's assume I want to purchase a magazine. I know that the average price is 4€. By checking how much money I carry, I can predict the likelihood that I can complete the purchase. By example:
- With 1€, the likelihood is low, maybe 0.1
- With 4€, the likelihood is average, 0.5
- With 50€, I can afford almost any magazine, 0.99
Now, I drew the probabilities and created a PDF (probability density function) out of nowhere. Knowing the actual repartition of magazine prices would help me create a more realistic PDF.
Q2: Is this intuitive approach correct?
Q3: How does it apply to the aforementioned problem in image processing?
Q4: Which topic/keywords is my question actually about? Can you recommend some sources / references to develop my knowledge?