"Tim " <tim.bate@gmail.com> wrote in message <hmq826$fc4$1@fred.mathworks.com>...
> In general, normalisation of a signal like image or audio is simply to have the signal stretch from the minimum to the maximum. As mentioned, though, there are other uses of the word, so you have to be careful. However, neither of yur formula seem to hit that definition, no matter how I twist it, so I'm not sure if that is really what you are after.
>
> Is we are talking about normalising a signal in the way mentioned above, then the first job is to get he signal into the range [0, 1], and then life becomes easy. So, in you case, and step by step (this can be reduced to a single line):
>
> 1) x2 = xmin(x) % Now you have the range [0, maxmin]
> 2) x3 = x2/(max(x)min(x)) % Now you have [0, 1].
> 3) x4 = x3*(newMax  newMin)
> 4) x5 = x4 + newMin
> 5) We are done!
>
> Note that often this simplifies dramatically; for example, in audio, we only modify the maximum, so it is one multiplication and we are done. Often we don't care about the lowest form of the signal, merely the maximum. Also, usually our new range begins at 0, so step 3 becomes x3*newMax, and we are done.
>
> So why do all this? Well, one reason may be because whatever we are outputting to expects a signal in a particular form. If you exceed 255 and are going into an 8bit device, your signal will be "clipped" (if you are lucky) and this is bad.
>
> Another reason may just be to "see" the signal better. The difference between two grey levels that vary by one is trivial. This is one way of "stretching" the signal out, in a lossless fashion.
>
> Finally, data may be more comparable if both sets are normalized first.
>
> P.S Please forgive changing between normalise and normalize. I'm an Aussie on a US spell checker.
>
> P.P.S All of this may be completely up the wrong tree for you. In which case, sorry for cluttering up your replies!
>
>
> "Vinod " <vinodkaruvat@gmail.com> wrote in message <hmo2h3$a94$1@fred.mathworks.com>...
> > Hello Friends,
> >
> > What is normalization(for image and a signal)?
> > What I understand is for eg a signal x = [1 3 5 7 9 11].
> >
> > 1) Is it norm_x = x/sum(x) or norm_x = x/sum(x). If it is abs values of x, why is it so?
> >
> > 2) In case of images how do I approach this task? Should I first take the normalization of rows and then follow it with that of columns? Or would normalization of rows serve my purpose? Is it then necessary to move into column processing? Would it involve dividing of each pixel by the sum of the row(relevant0 or by the whole image sum?
> >
> > 3) What is the need/use and application of normalization. What data can I get from image normalization?
> >
> > Regards,
> > Vinod Karuvat.
Hello Sir,
Thank u very much for your reply. It was very informative. How can this be applied to a 2D image?
How can normalization be made use of in the 2D domain? For example if I want to compute the probability density function I would need to first normalize the matrix/image and so on and so forth.
Likewise could you specify the other applications of normalization. Also could you please paste some links of normalization(I am looking for the math and also examples) please.
Again thank you very much.
Regards,
Vinod.
