Okay I am starting to understand the idea of neural networks but I still haven't been able to understand the use/benefits/implementation of convolutional neural networks especially for image processing.
What I don't understand is how to detect an object of different size.
Say we have a weights matrix of 50*50 and a face in the training set which is of 50*50 size . Now when you take the weighted sum of the matrix it will return a particular value say "X" . So now I understand the idea is to run the weights matrix over the entire image to get a lock over a region which gives a weighted sum of ~"X" now you have detected a face.
If the above mentioned understanding is true Then how is that an image of size 25*25 going to churn out a value anywhere clone to "X" . So the basic question is how to deal with relative sizes of the image .
If possible please suggest some good tutorials for the same .