I am planning to implement a pattern matching algorithm using something like correlation as a matching metric.
I know that the template I am going to use will, if present, have different sizes in the target images. The interval of sizes might be something like : x 0.5 , x 0.75 , x 1.0 , x 1.25 , x 1.5 , x 2.0 of the original template size.
To speed up the search it is suggested in various articles to build a gaussian pyramid of both the template and the target search image.
However I am not able to relate in any meaningful way the number of pyramid levels to create (for model and for target) , the object scales as described above, and the gaussian filter sigmas (used when creating the pyramid) !
Could someone shed any light on this ?
For example 1 level of the pyramid reduces the resolution of the image by 2 (is this the same as saying that the image has been scaled by 0.5 ) ?
Than what about a pyramid level of -1 (which should make the image bigger x 2.0) : with what gaussian filter should it be treated before upsampling ?
What about object scales (e.g. x 1.25) which fall in-between pyramid levels ?
Thanks to anyone who could provide some insight/references, Todor