Skip to main content

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

What does the output of imregcorr mean?

 I am trying to understand the output of imregcorr and could use some help. Below is the code I am working with. I have my own function called RegisterViaReddy that uses the technique explained in the reference of imregcorr to register images that differ in translation and rotation (I wrote my code before imregcorr was released). Unfortunately I cannot post RegisterViaReddy, but I understand its behavior so hopefully its details are not relevant.

 
Here is the sample code I am working with:
 
%%Start with a clean workspace
clear all;close all;clc;%#ok

%%Load image
fixedFull  = double(imread('cameraman.tif'));
rows = 30:226;
cols = rows;
fixed = fixedFull(rows,cols);

%%Specify motion parameters
theta = 5;%degrees
rowshift = 1.65;%pixels
colshift = 5.32;%pixels

%%Create rotated/translated image
RT = @(img,colshift,rowshift,theta) imrotate( imtransform(img, maketform('affine', [1 0 0; 0 1 0; colshift rowshift 1]), 'bilinear', 'XData', [1 size(img,2)], 'YData', [1 size(img,1)], 'FillValues', 0),theta,'crop'); %#ok
movingFull = RT(fixedFull, colshift, rowshift, theta);
moving = movingFull(rows,cols);

%%Show both images
figure;
imshowpair(moving,fixed,'montage');

%%Register images
[rowshift1, colshift1, theta1, imgReg] = RegisterViaReddy(fixed, moving);
tform1 = imregcorr(moving, fixed, 'rigid');

The function handle RT first translates an image and then rotates it. The resulting image is the same size as the input image. The outputs of my own RegisterViaReddy function are

>> [rowshift1, colshift1, theta1]

ans =

     -1.7600   -5.1000  -5.3402

These are nearly the opposites of the known rowshift, colshift, and theta parameters. I wrote my code this way so that

RT(moving,colshift1,rowshift1,theta1);

generates something that looks like the fixed image.

I do not understand how to get these parameters from the output of imregcorr (tform1). I understand that acosd(tform1.T(1,1)) is 5.1799 and is hence the rotation angle. However, tform1.T is

    0.9959    0.0903         0
   -0.0903    0.9959         0
    4.1423  -10.3337    1.0000

How do I extract meaningful translation parameters from this? I know I can generate something that looks like the fixed image using

imwarp(moving, tform1);

but the resulting array is 214x214 whereas fixed and moving are 197x197. Is there any way to get the translation offsets that I input from the output of imregcorr?


 NOTE:-


Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

I had been meaning to answer this question days ago and I finally came up for air. Hopefully this answer will still be of use.
There are several issues at play here:
 
1) The Image Processing Toolbox uses a different convention for the transformation matrix than many references you will find (they are a transponse of each other). The IPT convention is the transpose of many reference sources and is:
 
% Define a pure transformation, apply this transformation to input point (w,z) = (0,0) 
tx = 1.65;
ty = 5.32;
T = [1 0 0; 0 1 0; tx ty 1];
w = 0;
z = 0;
xy = [w z 1]*T

This means that for a rigid transformation, the tform object returned by imregcorr is off the form:

 tform = [cos(theta) sin(theta) 0; sin(theta) -cos(theta) 0; tx ty 1];
With the rotation matrix in the upper 2x2 and the translation in the last row.
 
2) In the operation you are synthetically applying to your input image and then attempting to recover, you apply a translation via imtransform and THEN you perform a rotation by using imrotate.
 
The transformation matrix returned by imregtform is an affine transformation consisting of a linear portion A (the upper 2x2 which includes rotation and scale) and an additive portion b (the last row which applies the translation.

Comments

Popular posts from this blog

https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing

Why are Fourier series important? Are there any real life applications of Fourier series?

A  Fourier series  is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms. For functions that are not periodic, the Fourier series is replaced by the Fourier transform. For functions of two variables that are periodic in both variables, the trigonometric basis in the Fourier series is replaced by the spherical harmonics. The Fourier series, as well as its generalizations, are essential throughout the physical sciences since the trigonometric functions are eigenfunctions of the Laplacian, which appears in many physical equations. Real-life applications: Signal Processing . It may be the best application of Fourier analysis. Approximation Theory . We use Fourier series to write a function as a trigonometric polynomial. Control Theory . The F