I'm trying to consistently detect a certain color between images of the same scene. The idea is to recognize a set of object based on a color profile. So, for instance, if I'm given a scene with a green ball in it and I select that green as part of my color palette, I would like a function which has a matrix reflecting that it detects the ball.
Can anyone recommend some matlab functions/plugins/starting points for this project? ideally the function for color recognition will take an array of color values and will match them within a certain threshold.
Kinda like this:
http://www.mathworks.com/matlabcentral/fileexchange/18440-color-detection-using-hsv-color-space-training-and-testing
except it works (this one didn't)
Update:
Here's why I chose not to use the above toolkit..
I start by selecting some colors of interest in the picture
and then ask the function to recognize the road in later images...
And absolutely nothing useful is triggered. So yeah, apart from the few bugs that I came across in the code on download and fixed, this was kind of the kicker. I didn't try to fix the body of the code that recognizes the colors because.. well, I don't know how, which is why I came here.
So, let me just start off by saying road detection with color profiles is a pathological problem. But if the color of the roads are consistent, and the lighting doesn't change the color of the object you are trying to recognize then you might have a shot. (this will be extremely difficult if this is taken outside, or with different cameras, or if shadows happen, or it taking place in any sort of real-world environment)
Here are a few things that might help.
Try smoothing the image beforehand, the reason you get the bad results in the first images is probably because of small pixel variations in the road. If you can blur them, or use some sort of watershed or local averaging, you might get regions with more consistent color.
You might also consider using the LAB color space instead of HSV or RGB.
Using edge detection (see matlab's canny edge detector) might be able to get you some boundary information. If you are looking for a smooth object, there will not be very many edges in it.
Edit: I tried to adhere to this advice in the most simplistic way. Here are the resulting code and a few samples.
im=rgb2gray(im) %for most basic color capturing.. using another color space is better practice
%imshow(im)
RoadMask=roipoly(im)%create mask
RoadMask=uint8(RoadMask);%cast to so you can elementwise multiply
im=im.*RoadMask;%apply mask
[x y]=size(im);
for i=1:x
for j=1:y
%disp('here')
if (im(i,j)<160 || im(i,j)>180) %select your values based on your targets range
im(i,j)=0; %replace everything outside of range with 0
%disp(im(x,y)) %if you'd like to count pixels, turn all values
end %within range to 1 and do a sum at end
end
end
First converted from RGB to grayscale
selected a region that generally matched the roads grayness
Notice parts of the road are not captured and the blocky edges. such as this -------------^
This implementation was quicky and dirty, but I wanted to put it up before I forgot. I'll try to update with code that implements smoothing, sampling, and the LAB color space.
Related
I have several images that i would like to correct from artifacts. They show different animals but they appear to look like they were folded (look at the image attached). The folds are straight and they go through the wings as well, they are just hard to see but they are there. I would like to remove the folds but at the same time preserve the information from the picture (structure and color of the wings).
I am using MATLAB right now and i have tried several methods but nothing seems to work.
Initially i tried to see if i can see anything by using an FFT but i do not see a structure in the spectrum that i can remove. I tried to use several edge detection methods (like Sobel, etc) but the problem is that the edge detection always finds the edges of the wings (because they are stronger)
rather than the straight lines. I was wondering if anyone has any ideas about how to proceed with this problem? I am not attaching any code because none of the methods i have tried (and described) are working.
Thank you for the help in advance.
I'll leave this bit here for anyone that knows how to erase those lines without affecting the quality of the image:
a = imread('https://i.stack.imgur.com/WpFAA.jpg');
b = abs(diff(a,1,2));
b = max(b,[],3);
c = imerode(b,strel('rectangle',[200,1]));
I think you should use a 2-dimensional Fast Fourier Transform
It might be easier to first use GIMP / Photoshop if a filter can resolve it.
I'm guessing the CC sensor got broken (it looks to good for old scanner problems). Maybe an electric distortion while it was reading the camera sensor. Such signals in theory have a repeating nature.
I dont think this was caused by a wrong colordepth/colorspace translation
If you like to code, then you might also write a custom pixel based filter in which you take x vertical pixels (say 20 or so) compare them to the next vertical row of 20 pixels. Compare against HSL (L lightnes), not RGB.
From all pixels calculate brightness changes this way.
Then per pixel check H (heu) is within range of nearby pixels take slope average of their brightness(ea take 30 pixels horizontal, calculate average brightnes of first 10 and last 10 pixels apply that brightness to center pixel 15,... //30, 15, 10 try to find what works well
Since you have whole strokes that apear brighter/darker such filter would smooth that effect out, the difficulty is to remain other patterns (the wings are less distorted), knowing what color space the sensor had might allow for a better decision as HSL, maybe HSV or so..
Here are some images taken from experiments which show a bubble caused by spheres moving in liquid.
Now I want to get the area of the bubble from every image using Matlab. The first thing come to my mind is edge detection. So I tried using the following code:
A = imread('D:\1.jpg');
BW1 = edge(A,'sobel');
figure, imshow(BW1)
to get the cavity edge of the picture which was then cropped manually, as the picture show, the result (below) doesn't satisfy requirements. Also, I still don't know how to get the area of the bubble.
So, can someone tell me what should I do?
I think you should use background subtraction and try a simple segmentation.
You could use regionprops to get the area of the bubble:
https://www.mathworks.com/help/images/ref/regionprops.html
I feel like it should work pretty well. If you have a hard time obtaining a clean segmentation you could probably improve the experimental setup to increase the contrast of the bubble with respect to the background by choosing a background as dark as possible and using some lateral illumination to leverage the diffusion of the light by the bubble.
Finally the segmentation should be performed in a region of interest (ROI) since you know the bubble is confined within the tank
As for the issue of getting an accurate cavity edges, the computer vision system toolbox has the vision.ForegroundDetector object, which implements a variant of Stauffer and Grimson's GMM background subtraction. The implementation is very fast, leveraging multiple cores. Check out this example of how to use background subtraction.
As for the issue of finding the area of the bubble, use the bwarea command. https://www.mathworks.com/help/images/ref/bwarea.html, it will sum up all the white pixels in the image.
I believe background subtraction is the most efficient method to calculate this bubble area. Note that you may need to use opening and closing techniques afterwards to filter other regions see (imopen imclose) at: https://uk.mathworks.com/help/images/ref/imopen.html , and afterwards, you can apply bwarea to calculate area. You could also use impixelinfo command to compare intensity level of bubbles and other areas, and therefore, threshold image to extract bubbles. It works only when you have same threshold level for all images. Further, it is possible to combine all these techniques which is completely depended on your images to achieve better results.
Other shape-based techniques also can be used to extract bubble region area.
I have an image (see attached) and I am trying to calculate the variance of the image inside the region of interest (dark region) using the stdfilt function.Image here.
The dark side is what I need to work on. When I use stdfilt on this image, it shows me the boundaries of the dark and bright.
My idea is that we can threshold the image to show only the dark side and tell Matlab to work only with this region of interest. So far, did not find a proper way of doing this.
The area is not a perfect polygon, which would make things way easier. At that point, I'm not sure what to do, so any suggestions are welcome.
Cheers
If the spatial location of the pixels is not relevant, you could just do:
datatoprocess=I(I<threshold);
Being threshold a value that separates the white from black. [graythresh][1] is a fantastic function for that. datatoprocess will be a 1xN array with the pixel values.
If, instead, the spatial location of the pixels is relevant, then you need to modify your functions to not work on specific pixels. The best approach for this is generally setting NaN values in pixels you dont want to take into account.
Itoprocess=I;
Itoprocess(I>threshold)=NaN;
Without more information on what exactly are you doing with the image, this is the best anyone can get to.
i want to get the red region as specified in the image below :
remember that the red region that is shown in the image is just for clarification , it is not present in original image , below is the original image attached :
i also have the iris point in this region, i already got that point , if that point can help me so i can share that image too.
can someone help me in this .....
For this specific image, let's call it BW, you can find the center region as:
BWnoBorder= imclearborder(BW); %# remove the white that touches the border
OnlyCenter = bwareaopen(BWnoBorder,1000); %# remove all small pixel areas
A robust method might be the snake region growing algorithm.
Seems like you thresholded the eye illuminated by IR or something. To answer your question or even to ask it correctly you have to show a number of images to evaluate the stability and noise in eye socket regions. Otherwise one can come up with a solution that works for the image above but not in general.
For example, I can invert your image, get the largest Connected Component (the dark region) and erode it till it becomes thin, see below. It is easy to get an ellipse from this binary mask but will it work in general case with your noisy input?
A good thing to start is to say what do you expect to find. Say you are looking for an eye, dark surrounding area and a bright skin tone - make it 3 Mixed models that settle simultaneously in the EM fashion. Provide some shape priors to increase accuracy. think about other visual cues such as specs on the iris, saccades, FA from blinks, etc.
I have several images of the pugmark with lots of irrevelant background region. I cannot do intensity based algorithms to seperate background from the foreground.
I have tried several methods. one of them is detecting object in Homogeneous Intensity image
but this is not working with rough texture images like
http://img803.imageshack.us/img803/4654/p1030076b.jpg
http://imageshack.us/a/img802/5982/cub1.jpg
http://imageshack.us/a/img42/6530/cub2.jpg
Their could be three possible methods :
1) if i can reduce the roughness factor of the image and obtain the more smoother texture i.e more flat surface.
2) if i could detect the pugmark like shape in these images by defining rough pugmark shape in the database and then removing the background to obtain image like http://i.imgur.com/W0MFYmQ.png
3) if i could detect the regions with depth and separating them from the background based on difference in their depths.
please tell if any of these methods would work and if yes then how to implement them.
I have a hunch that this problem could benefit from using polynomial texture maps.
See here: http://www.hpl.hp.com/research/ptm/
You might want to consider top-down information in the process. See, for example, this work.
Looks like you're close enough from the pugmark, so I think that you should be able to detect pugmarks using Viola Jones algorithm. Maybe a PCA-like algorithm such as Eigenface would work too, even if you're not trying to recognize a particular pugmark it still can be used to tell whether or not there is a pugmark in the image.
Have you tried edge detection on your image ? I guess it should be possible to finetune Canny edge detector thresholds in order to get rid of the noise (if it's not good enough, low pass filter your image first), then do shape recognition on what remains (you would then be in the field of geometric feature learning and structural matching) Viola Jones and possibly PCA-like algorithm would be my first try though.