I want to write a code in matlab in which i would like to detect color objects in a given image and return the result as found the custom image or not found. i have an image of the custom object separately. im new to matlab... can anyone tell me how to proceed...
i have a pre defined image of an object say an lcd tv.... a given image which may or may not contain the object in it. i need a method to chek and find if the pre defined image is present or not in the given image... is it possible in matlab?
The I suggest you start researching. Stack Overflow is not a great place to ask such generalized questions.
1) Anything that you can do in any programming language is possible in matlab, you are limited by three things:
a) How fast do you want it to work
b) how much coding do you want to do
c) how much of your memory is matlab going to eatup
2) If I am understanding your question correctly you are: Looking to match an image that is fit inside a larger image
Solution: shift your image across the other image, calulate the difference between all the pixels. The set of pixels that is closest to your desired image should be zero.
Related
I have troubles extracting a tumor using a RT mask from a dicom image. Due to GDPR I am not allowed to share the dicom images even though they are anonymized. However I am allowed to share the images themself. I want to extract the drawn tumor from the CT images using the draw GTV stored as a RT structure using MATLAB.
Lets say that the file directory where my CT images are stored is called DicomCT and that the RT struct dicom file is called rtStruct.dcm.
I can read and visualize my CT images as follows:
V = dicomreadVolume(“DicomCT”);
V = squeeze(V);
volshow(V)
volume V - 3D CT image
I can load my rt structure using:
Info = dicominfo(“rtStruct.dcm”);
rtContours = dicomContours(Info);
I get the plot giving the different contours.
plotContour(rtContours)
Contours for the GTV of the CT image
I used this link for the information on how to create the mask such that I can apply it to the 3D CT image: https://nl.mathworks.com/help/images/create-and-display-3-d-mask-of-dicom-rt-contour-data.html#d124e5762
The dicom information tells mee the image should be 3mm slices, hence I took 3x3x3 for the referenceInfo.
referenceInfo = imref3d(size(V),3,3,3);
rtMask = createMask(rtContours, 1, referenceInfo)
When I plot my rtMask, I get a grey screen without any trace of the mask. I think that something is wrong with the way that I define the referenceInfo, but I have no idea how to fix it or what is wrong.
volshow(rtMask)
Volume plot of the RT mask
What would be the best way forward?
i was actually having some sort of similar problem to you a couple of days ago. I think you might have two possible problems (none of them your fault).
Your grey screen might be an error rendering that it's not showing because of how the actual volshow() script works. I found it does some things i don't understand with graphics memory and representing numeric type volumes vs logic volumes. I found this the hard way in my job PC where i only have intel HD graphics. Using
iptsetpref('VolumeViewerUseHardware',true)
for logical volumes worked fine for me. You an also test this by trying to replot the mask as double instead of logical by
rtMask = double(rtMask)
volshow(rtMask)
If it's not a rendering error caused by the interactions between your system and volshow() it might be an actual confusion and how the createMask and the actual reference info it needs (created by an actual bad explanation in the tutorial you just linked). Using pixel size info instead of actual axes limits can create partial visualization in segmentation or even missing it bc of scale. This nice person explained more elegantly in this post by using actual geometrical info of the dicom contours as limits.
https://es.mathworks.com/support/search.html/answers/1630195-how-to-convert-dicom-rt-structure-to-binary-mask.html?fq%5B%5D=asset_type_name:answer&fq%5B%5D=category:images/basic-import-and-export&page=1
basically use
plotContour(rtContours);
ax = gca;
referenceInfo = imref3d(size(V),ax.XLim,ax.YLim,ax.ZLim);
rtMask = createMask(rtContours, 1, referenceInfo)
In addition to your code and it might work.
I hope this could be of help to you.
I kind of reverse engineered an image filter. Actually it was just a pixel by pixel operation so I applied it over different images and by comparing each pixel of original image and filtered image (using PIL) I now know what RGB value from original image became what RGB in the filtered image. For example like RGB(0,0,1) became RGB(2,3,81) (suppose) etc and I know this for all 16,777,216 colors.
If what I did is correct then my question is how can I create a filter out of this data that can be used in flutter apps. One option is to use conditional statements but that's just theoretical as I'll have to manually write 16,777,216 statements just for this one filter. So is there any software or program or code or anything else that I can use to create a filter out of this data that can be used in flutter apps. This is important because ultimately I want to use this filter in my flutter app.
Any help would be much appreciated.
Thank you very much.
I'm a newbie to Matlab. I'm basically attempting to manually segment a set of images and then manually label those segments also. I looked into the imfreehand(), but I'm unable to do this using imfreehand().
Basically, I want to follow the following steps :
Manually segment various ROIs on the image (imfreehand only lets me draw one segment I think?)
Assign labels to all those segments
Save the segments and corresponding labels to be used further (not sure what format they would be stored in, I think imfreehand would give me the position and I could store that along with the labels?)
Hopefully use these labelled segments in the images to form a training dataset for a neural network.
If there is some other tool or software which would help me do this, then any pointers would be very much appreciated. (Also I am new to stackoverflow, so if there is any way I could improve on the question to make it clearer, please let me know!) Thanks!
Derek Hoiem, a computer vision research at the University of Illinois, wrote an object labelling tool which does pretty much exactly what you asked for. You can download it from his page:
http://www.cs.illinois.edu/homes/dhoiem/software/index.html
I would like to check whether an image has a lot of homogeneous areas. Therefore I would like to get some kind of value of an image that declares a ratio for images depending on the amount/size of homogeneous areas (e.g. that value could have a range from 0 to 5).
Instead of a value there could be some kind of classification as well.
[many homogeneous areas -> value/class 5 ; few homogeneous areas -> value/class 0]
I would like to do that in perl. Is there a package/function or something like that?
What you want seems to be an area of image processing research which I am not familiar with. However, GraphicsMagick's mogrify utility has a -segment option:
Use -segment to segment an image by analyzing the histograms of the color components and identifying units that are homogeneous with the fuzzy c-means technique. The scale-space filter analyzes the histograms of the three color components of the image and identifies a set of classes. The extents of each class is used to coarsely segment the image with thresholding. The color associated with each class is determined by the mean color of all pixels within the extents of a particular class. Finally, any unclassified pixels are assigned to the closest class with the fuzzy c-means technique.
I don't know if this is any use to you. You might have to hit the library on this one, and read some research. You do have access to this through PerlMagick as well. However, it does not look like it gives access to the internals, but just produces an image based on parameters.
In my tests (without really understanding what the parameters do), photos turned entirely black, whereas PNG images with large areas of similar colors were reduced to a sort of an average color. Whether you can use that fact to develop a measure is an open question I am not going to investigate ;-)
I'm trying to do top hat filtering in MATLAB. The imtophat function looks promising, but I have no idea how to use it. I don't have a lot of work with MATLAB before. I am trying to look find basically small spots several pixels wide that are local maxima in my 2 dimensional array.
I think you have more problem undertanding how to use STREL, than IMTOPHAT. The later can be described as simple threshold but per structural element, not the whole image.
Here is another good examples of using STREL and IMTOPHAT:
http://www.mathworks.com/matlabcentral/fx_files/2573/1/content/html/R14_MicroarrayImage_CaseStudy.html
This series of posts on Steve Eddins blog might be useful for you:
http://blogs.mathworks.com/steve/category/dilation-algorithms/
tophat is basically an "opening" procedure followed by a subtraction of the result from the original image. the best and most helpful explanation of opening I've found here:
http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm
"The effect of opening can be quite easily visualized. Imagine taking
the structuring element and sliding it around inside each foreground
region, without changing its orientation. All pixels which can be
covered by the structuring element with the structuring element being
entirely within the foreground region will be preserved. However, all
foreground pixels which cannot be reached by the structuring element
without parts of it moving out of the foreground region will be eroded
away."
The documentation on imtophat has an example .. did you try it? The following images are from the MATLAB documentation.
Code
I = imread('rice.png');
imshow(I)
se = strel('disk',12);
J = imtophat(I,se);
figure, imshow(J,[])
Original
(image source: mathworks.com)
Top Hat with a disk structuring element
(image source: mathworks.com)