# A Filter is Sum normalized to 0 and square normalized to 1

Firstly, About the Hierarchical Model And X (HMAX model), i still don't understand what is the difference between the "Original model" and "Standard model". Does in Standard, we use imfilter not conv2 ? is that correct ?

Secondly, i didn't understand the following phrase : "The filters in S1 layer are sum-normalized to zero and square-normalized to 1, and the result of the convolution of an image patch with a filter is divided by the power (sum of squares) of the image patch. "

What was meant by sum-normalized and square normalized ? for example : f=f./ sqrt(sum(sum(f.^2))).

Please i need your help and explanations. Any help will be very appreciated.

• Hi Liszt, and welcome to scicomp! While signal processing questions do often overlap with computational science, we don't have a large base of signal processing experts in this forum. It may be worthwhile to migrate this question to the Digital Signal Processing SE site. You may get a better and quicker response there. – Paul Dec 28 '13 at 14:47
• These phrases are equivalent to some statistical manipulations you may already be familiar with. "sum-normalized to zero" amounts to subtracting off the mean. "square-normalized to 1" would follow that first step, producing (if the original data wasn't constant) data with variance 1 (about its now zero mean). – hardmath Dec 28 '13 at 15:03

• In OP's notation f=f-sum(f); f=f./sqrt(sum(f.^2)) or f=f-sum(f); f=f./norm(f) – k20 Dec 28 '13 at 19:17
• Not quite; rather than subtracting sum(f) from each entry, we need to subtract the average, sum(f)/(# of points). – hardmath Dec 28 '13 at 19:41