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?