Write an m-file "distan.m"  which does all the following operations:

(i) computes 10 dimensional histogram of an input image
            h1=sum(hist(imn,10)');

(ii) computes negative image and  its histogram

(iii) normalizes  vectors h1, h2 such that the integral of each is 1
Why does it have to be normalized?

(iv) displays both histograms

(v) computes similarity between two histograms using
            euclidean distance,
            histogram intersection,
            correlation,
            chi-square

function distan(im)
imn=imnorm(im)/255.0;
h1=?
h1n=?

imnn=?
h2=?
h2n=?

%euclidean
he=sqrt(sum((h1-h2).^2))

%correlations
hc=?

%intersection, use min function
hh=?

%chi-square, use sum function
hch=?

How do the distances differ?