I have a gray scale image where I have marked the pixels from 1 - 16.
I want to visualize different intensity pixels in different shades of colors. What is the easiest way to do it such that these numbers 1-16 can be anything ( for eg 5,10,20 ).
I have looked at colormap but am unable to use it in this fashion.
Related
im trying to identify a specific shade of green leaves (e.g. navy green) from the attached image. how do i do that in the most efficient way? So far, i'm converting the RGB to HSV and then thresholding the image based on some specific range of saturation and value that will isolate my desired shade. it's working on some images and it's just all over the place on others. i want something that can isolate a specific shade of green in any different image that has slightly different saturation and value (e.g. if the picture was taken with too much light)
Image link
pic=imread('image.jpg');
q=rgb2hsv(pic);
H=q(:,:,1);
S=q(:,:,2);
V=q(:,:,3);
thresh=S>0.6111 & S<0.6666 & V>0.3888 & V<0.4583;
st=strel('diamond',20);
w=imdilate(thresh,st);
comps=bwconncomp(w,8);
num=comps.NumObjects;
fprintf('The number of leaves is %i',num)
% then i try to have some pointers on the image to show me where matlab has identified the the shade.
m = regionprops(w,'centroid');
boxes = cat(1, m.Centroid);
imshow(pic)
hold on
plot(boxes(:,1),boxes(:,2), 'b*')
hold off
Your help will be highly appreciated.
Either the HSV color space (hey, S is saturation and V value), where H will give you the hue,or CIE-Lab color space, where euclidean distance will give you how close 2 specific pixel are to each other in color.
This answer explains how to do it for HSV: Segment pixels in an image based on colour (Matlab)
Using combined with CIE-LAB may help if the colors are very close together (like the greens in each leaf), but you should give HSV a shot
I'm learning about statistical feature of an image.A quote that I'm reading is
For the first method which is statistical features of texture, after
the image is loaded, it is converted to gray scale image. Then the
background is subtracted from the original image. This is done by
subtract the any blue intensity pixels for the image. Finally, the ROI
is obtained by finding the pixels which are not zero value.
The implementation :
% PREPROCESSING segments the Region of Interest (ROI) for
% statistical features extraction.
% Convert RGB image to grayscale image
g=rgb2gray(I);
% Obtain blue layer from original image
b=I(:,:,3);
% Subtract blue background from grayscale image
r=g-b;
% Find the ROI by finding non-zero pixels.
x=find(r~=0);
f=g(x);
My interpretation :
The purpose of substracting the blue channel here is related to the fact that the ROI is non blue background? Like :
But in the real world imaging like for example an object but surrounded with more than one colors? What is the best way to extract ROI in that case?
like for example (assuming only 2 colors on all parts of the bird which are green and black, & geometri shaped is ignored):
what would I do in that case? Also the picture will be transformed to gray scale right? while there's a black part of the ROI (bird) itself.
I mean in the bird case how can I extract only green & black parts? and remove the rest colors (which are considered as background ) of it?
Background removal in an image is a large and potentielly complicated subject in a general case but what I understand is that you want to take advantage of a color information that you already have about your background (correct me if I'm wrong).
If you know the colour to remove, you can for instance:
switch from RGB to Lab color space (Wiki link).
after converting your image, compute the Euclidean from the background color (say orange), to all the pixels in your image
define a threshold under which the pixels are background
In other words, if coordinates of a pixel in Lab are close to orange coordinates in Lab, this pixel is background. The advantage of using Lab is that Euclidean distance between points relates to human perception of colours.
I think this should work, please give it a shot or let me know if I misunderstood the question.
I'm trying to create a true color RBG image from satellite data using matlab, but I don't know how to do it.
The false color RGB image is simple, just employing the right channels for the red, green and blue you can make it
RGB(:,:,1)=(ref16)'; %red - reflectance 1.6mic
RGB(:,:,2)=(ref06)'; %green - reflectance 600nm
RGB(:,:,3)=(ref05)'; %blue - reflectance 500nm
image(RGB)
In this case I'm using reflectances from the satellite channels which range from 0 to 1, so I don't need to modify the original data
But I'm having so much trouble when I try to plot true color images.
According to literature, the following profile should yield good RGB images from MERIS Level-1b data products (the data I'm using). The linear-combinations for the red, green and blue components are based on the colour matching functions of the CIE 1931 color space.
RGB(:,:,1)=log(1.0+0.35*radiance_2+0.60*radiance_5+radiance_6+0.13*radiance_7)'
RGB(:,:,2)=log(1.0+0.21*radiance_3+0.50*radiance_4+radiance_5+0.38*radiance_6)'
RGB(:,:,3)=log(1.0+0.21*radiance_1+1.75*radiance_2+0.47*radiance_3+0.16*radiance_4)'
Radiance are real values going from 0 to 400 (with the scale factor applied), so I guess that I have to normalize the RGB array (0-1 or 0-255) to create the image.
But doing the normalization myself or just using im2uint8 doesn't produce the right image.
It's likely that I'm doing everything wrong because I'm not familiar with colour profiles. Is there a way in matlab to create the image using directly the CIE rgb combination (the one I think I'm getting from the above formulas)?
Is anyone out there familiar with images using matlab and satellite data?
Thanks!
we are doing a mat lab based robotics project.which actually sorts objects based on its color so we need an algorithm to detect specific color from the image captured from a camera using mat lab.
it will be a great help if some one can help me with it.its the video of the project
In response to Amro's answer:
The five squares above all have the same Hue value in HSV space. Selecting by Hue is helpful, but you'll want to impose some constraints on Saturation and value as well.
HSV allows you to describe color in a more human-meaningful way, but you still need to look at all three values.
As a starting point, I would use the rgb space and the euclidian norm to detect if a pixel has a given color. Typically, you have 3 values for a pixel: [red green blue]. You also have also 3 values defining a target color: [255 0 0] for red. Compute the euclidian norm between those two vectors, and apply a decision threshold to classify the color of your pixel.
Eventually, you want to get rid of the luminance factor (i.e is it a bright red or a dark red?). You can switch to HSV space and use the same norm on the H value. Or you can use [red/green blue/green] vectors. Before that, apply a low pass filter to the images because divisions (also present in the hsv2rgb transform) tend to increase noise.
You probably want to convert to the HSV colorspace, and detect colors based on the Hue values. MATLAB offers the RGB2HSV function.
Here is an example submission on File Exchange that illustrate color detection based on hue.
For obtaining a single color mask, first of all convert the rgb image gray using rgb2gray. Also extract the desired color plane from the rgb image ,(eg for obtaining red plain give rgb_img(:,:,1)). Subtract the given plane from the gray image........
Consider:
%# load a grayscale image
img = imread('coins.png');
%# display the image
figure
imshow(img,[]);
%# false-color
colormap('hot')
The above code is from here:
Infrared image processing in Matlab
But I don't understand how figure (what's the difference with/without it?) and colormap (how does it affect the already shown image?) work?
figure is not required, imshow just displays img on it. If a figure hadn't been opened, imshow would've created a new one.
The colormap colors the intensities of the image. The hot map colors values in increasing intensity with black, red, yellow, and white-hot. Another popular colormap is jet which has a number of interesting colors.
False colors
So the matrix you want to see has intensities which can have any range of values. For better visualization, the intensities are displayed in a range of colors or a set of false colors. Normally, a grayscale image will display an image is shades of grey, where white is maximum and black is minimum. False color is an extension of that concept with several colors in between (like jet) and an effect of metal being heated in hot.
Colormap at the pixel level
Suppose you have a matrix with pixel values ranging from [cmin xmax]. Now, normalize the values so that the range is [0,1]. Also, suppose you have a color map, such that a range of colors are mapped to some values from 0 to 1 (e.g. 0.5 is mapped to RGB(100,200,100))- then you get the false color mapping by finding the closest intensity in the map and display the corresponding color.
More on colormap in the MATLAB documentation. I've included some picture from that link here:
Jet
(source: mathworks.com)
Bone
alt text http://www.mathworks.com/access/helpdesk/help/techdoc/ref/bone_spine.gif