Skip to main content

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

Why am I getting an error when I use grayscale images with "makehdr"in Image Processing Toolbox 8.1 (R2012b)?

 I am receiving the following error when trying to use the "makehdr" utility.

 
low dynamic images must be RGB
 
 
I suspect the reason is that my images are black-and-white. Is there any workaround for this issue?

 NOTE:-


Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help , Finance 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.


"makehdr" works only on RGB images. The workaround for this issue is to convert the grayscale image to an RGB image.
Grayscale images are 2D (m x n) matrices, while RGB images are 3D (m x n x 3) matrices. Therefore one must expand the 2D matrix to fill the 3D matrix. This can be accomplished a couple ways
 
1. Use REPMAT to replicate the 2D matrix 3 times.
 
% Step 1 - Load grayscale image
imGray = imread('grayscaleImage.tif');

% Step 2 - Replicate image into RGB layers using REPMAT
nRV = 1; % Number Of Replications Vertically
nRH = 1; % Number Of Replications Horizontally
nRL = 3; % Number Of Replication Layers
imRGB  = repmat(imGray,[nRV, nRH, nRL]); % Replicate grayscale image 3 times

% Step 3 - Write resized image to file
imageName = 'rgbImage.tif';
imwrite(imRGB,imageName)

% Step 4 - Generate HDR from TIF File



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

What are some good alternatives to Simulink?

Matlabsolutions 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. SIMULINK is a visual programing environment specially for time transient simulations and ordinary differential equations. Depending on what you need there are plenty of Free, Libre and Open Source Software (FLOSS) available: Modelica language is the most viable alternative and in my opinion it is also a superior option to MathWorks SIMULINK. There are open source implementations  OpenModelica  and  JModelica . One of the main advantages with Modelica that you can code a multidimensional ordinary differential equation with algebraic discrete non-causal equations. With OpenModelica you may create a non-causal model right in the GUI and with

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