I am trying to calculate the area of vegetation in aerial image but I don't have the information regarding the height at which the image is taken. By using the RGB values, I've extracted the vegetation area in the image. The area that does not hold vegetation has been set to black (eg; (0,0,0)).
How do I proceed from this step?
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For a color image, say with dimensions 320 by 240, we have 76,800 pixels in the image. What does each pixel represent for a color image? Is it just the RGB values for that pixel? How are shapes and textures represented in pixels? If each pixel in a colored image only contains RGB values, is that information enough to store the shape, size, and texture of the objects in the image?
A single pixel in RGB space can only hold information about the color value of that single pixel.
Shapes, and textures can only be described with the combination of several pixels, that information is not stored in single pixels themselves.
Moreover this information (same as for shape, size, texture of possible objects) is never stored explicitly in the image data. You can infer shapes or textures based on your interpretation of underlying pixel data, but this always depends on how you yourself define a shape or a texture.
Every pixel contains a simplified representation of the light landing on the corresponding sensor cell in the camera. The amount of light is averaged over the cell area, and light spectrum is grossly described by taking three weighted averages of intensity over the frequencies. The result is (usually) three integers in the range 0-255, for a total of 24 bits of information.
As the pixels are aligned on a grid, a digital color image can be seen as a triple matrix of integers, that's it. (Below, an example of such a matrix.) This information is raw.
The semantic image content must be inferred by an image analysis system, which is able to segment the image in distinct areas, and to a lesser extent, characterize the textures.
I have segmented image in which my region of interest (ROI) was white color cotton. Now I want to compare the number of pixels in segmented area i.e. total number of pixels in white blob in binary image with actual number of pixels of ROI in actual image. How I can do that. Following figure can clear the point.
As we can see from original image, my ROI was white color cotton circled in red boundry. When I segmented this image I got binary image as shown. As we can noticed there are some missing areas in binary image as compare to original area. So, I want to count the number of pixels in original image of ROI and number of pixels of white blob in binary image. So that I can calculate difference in actual pixels of ROI and actual segmented number of pixels.
Thank You.
If you wish to not draw the boundaries yourself, you can try this. It might not be as precise as you need, but you might get close to the actual value by tweaking with the thresholding values I used (100 for all 3 channels in this case).
Assume I is your original image. First create the binary mask by thresholding with the RGB values. Then remove all the small objects that don't have at least a 2000 pixel area. Then sum up the pixels of that object.
IT = I(:,:,1) > 100;
IT(I(:,:,2) < 100) = 0;
IT(I(:,:,3) < 100) = 0;
IT = bwareaopen(IT, 2000);
sum(IT(:) > 0)
21380
Resulting image:
I have an image with 6 colors each indicating a value. I had obtained an image as shown below.
I need to smoothen the edges and then find out the area as well as the surface area of that region. The second image shows a black line drawn in the edges which indicates that I need to smoothen the edges in such a way.
I had used segmentation to create a mask as shown in the third image, and then obtain a segmented image using the code following the image.
I have used the following code for generating till the masked image.
Source : How to segment
imshow(Out1)
str = 'Click to select initial contour location. Double-click to confirm and proceed.';
title(str,'Color','b','FontSize',12);
disp(sprintf('\nNote: Click close to object boundaries for more accurate result.'));
mask = roipoly;
figure, imshow(mask)
title('Initial MASK');
maxIterations = 3000;
bw = activecontour(Out1, mask, maxIterations, 'Chan-Vese');
% Display segmented image
figure, imshow(bw)
title('Segmented Image');
In order to use the 'activecontour' function my image needs to be a grey-scale image, which I'm not being able to convert to greyscale and back. Also to find out surface area/ area of the region is there any inbuilt function. Please help thanks.
use im2double, im2uint8, etc. to convert binary image to grayscale.
use bwarea or regionprops to find the region area.
I am working with RGB images that contain a single object against a monochrome background.
My goal is to isolate the object in the image and resize the image to contain only the object.
I have successfully been able to detect the object by converting the image to a binary image using an appropriate threshold. Then, in order to isolate the object in the original RGB image I use the binary image as a mask with the original RGB image.
maskedImage = bsxfun(#times,originalimage, cast(binaryimage,class(originalimage)));
This leaves me with a image only containing the object surrounded by a black background. This is due to the fact that the binary image mask I used contained the object in white pixels and the background in black pixels and since possess intensity values of 0 the masking process converted all pixels that didn't belong to the object to black pixels. I've attached an example below.
I would now like to draw a bounding box around the object and resize the image to the size of the bounding box, so that I can get rid as much of the surrounding black pixels as possible. Is there any way of doing this? Any help would be appreciated.
Given the segmented image, you want to crop out all of the black pixels and provide the closest bounding box that fully encapsulates the object. That's very simple.
You already have a binary mask that determines what is an object and what's background. You simply need to find the minimum spanning bounding box. You can find the top-left and bottom right corner by obtaining all of the pixel locations that are non-zero in the mask, and finding the minimum and maximum row and column coordinates. You'd then just use these to crop out the segmented image.
As such:
%// Find all non-zero locations in the mask
[row,col] = find(binaryImage);
%// Find the top left corner of the mask
topLeftRow = min(row);
topLeftCol = min(col);
%// Find the bottom right corner of the mask
bottomRightRow = max(row);
bottomRightCol = max(col);
%// Extract the object
extracted = maskedImage(topLeftRow:bottomRightRow, topLeftCol:bottomRightCol, :);
The words of the day are Bounding boxes !
If you want the minimum-area rectangle to crop only the nonzero values, you want the bounding box of your region, then set your phasers to stun and you're all set !
See this Matlab help forum question for more implementation details in Matlab.
I was creating an effects library for a PhotoBooth App. I have created effects like Black/White, Vintage, Sepia, Retro etc. etc.
I wanted to create a few effects now in which I wanted to have a Dark Border at the edges which kind of form a frame for the image .. something like this -> Example Effect
How can I do this using Pixel Bender and Flash ?
The effect you are describing is called vignetting. It is basically just darkening the pixels with some weight that changes depending on distance from the center of the image. In image editing it corresponds to overlaying the image with black color and applying a circular or elliptic mask to it, for example:
(source: johnhpanos.com)
You can do this by several methods depending on how you operate with image and its pixels. For example by multiplying the pixels by a weight coefficient that is smaller when closer to the center and bigger when farther away from it. The distance can be calculated from the difference between pixel coordinates.