Kindly review my program for block matching ? How do I overcome its constraints?

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Hello all! Below is the code for block matching of two imags al and br(which is a sub-image of al). I divided al into 4 blocks, matched with br and computed SSd. Though the result was quite accurate, I have few questions:
  • In this case, my images blocks are divided exactly, what if my block sizes are different
  • This is not the most accurate algorithm. Is there any other way to implement "block matching algorithm"?
clear all;
close all;
al = imread('testimage.jpg');
br = uint8(zeros(125, 125, 3));
br(1:126, 1:126,:) = al(80:205, 100:225 , :)
imtool(al); imtool(br);
al= rgb2gray(al);
al = im2double(al);
br= rgb2gray(br);
br = im2double(br);
c(1:126 , 1:126, :) = al(1:126 , 1:126, :)
d(1:126, 1:126,:) = al(100:225, 100:225,:)
e(1:126, 1:126,:) = al(100:225, 1:126,:)
f(1:126, 1:126,:) = al(1:126,100:225, :)
imtool(c); imtool(d); imtool(e); imtool(f)
ssdc= 0; ssde=0; ssdd=0; ssdf=0;
diffc = 0; diffd = 0; diffe=0; difff=0;
for( k = 1:1:126)
for(l = 1:1:126)
diffc = c(k,l) - br(k,l);
ssdc = ssdc+ (diffc*diffc);
diffd = d(k,l) - br(k,l);
ssdd = ssdd+ (diffd*diffd);
diffe = e(k,l) - br(k,l);
ssde = ssde+ (diffe*diffe);
difff = f(k,l) - br(k,l);
ssdf = ssdf+ (difff*difff);
end
end
y = [ssdc ssdd ssde ssdf] ;
min(y)
<<
>> The sub-block is the image d, which is a part of image a. I got the minimum cost at d. Kindly help me solve the above problem. Thank you!!

Answers (1)

Image Analyst
Image Analyst on 25 Sep 2014
You can use normalized cross correlation. See demo attached below the image it creates.

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