I'm looking for an algorithm to detect circles in an image. The image is black and white. The background is white, and the circles don't overlap each other, or any other element in the image.
The image includes some other shapes and some text.
If there is some open source .NET library to do this, I would also like to know about it.
Maybe the "Hough Transform" is useful for you. You have to know the circle's size in advance to make it efficient though.
http://www.cis.rit.edu/class/simg782/lectures/lecture_10/lec782_05_10.pdf
http://en.wikipedia.org/wiki/Hough_Transform
There was a similar question yesterday, where the "Hough Transform", and some image processing libraries (though not for .NET) were proposed:
Image Processing Programming
I was looking for the same thing and what I have found to work best for now is using Mathlab (Image Processing Toolbox). It has good amount of options that lets you try different processing algorithms, threshold level and range of circle's radius.
Related
I have a picture of notebook (with squares) and lines and dots are drawn in it like in the description. Output should be a data structure which contains info about boundaries and dots. How one can accomplish that? If possible, program should process this dynamically (given a video).
Yes this can be accomplished by various image processing techniques.
One famous technique that can help is called the Canny Edge Detector. It can detect all the defined edges within an image. More can be looked into it here. Various python and C# image processing libraries make this extremely easy. Take for example OpenCV
For detecting dots in the middle of the edges, that would be up to you to come up with, unless anyone knows of a library to make that easy as well. I suggest looking at each square that we detected by the canny edge detector and see if there are any dark color values around the middle.
For the data structure, that is also up to you.
Remember that a video is just a sequence of images. Just apply the same technique to all the images.
I'm working on an algorithm that counts patterns (bars) in a specific image. It seemed to me very simple at the first look, but I realized the complexity quickly.
I have tried simple thresholding, template matching (small sliding windows), edge detection...
I have just few images like this one. so I think that a machine learning algorithm can't give better results! but I still need suggestions.
I think you have enough data from your images. You need to crop from your images only the bars. You would get several dozens of small images for each image. After that you can resize all the images to some predefined size (for example 24X24 pixels) use a descriptor like HOG and SVM for the learning. For the false just use any other areas from your images.
This may not work in all cases, but since these are round bars, you can also try using circle detection. Both matlab(find circles) and opencv(hough circle transform) support this hough circle transformation. One issue is that you have to play with the parameters a bit (matlab is more simplistic than open cv) but that is true of almost any method.
These methods work better with larger images so I resized yours. You also need to know the radius of the circles to look for. If your camera position is constant, this shouldn't change much. This code is taken from the matlab documentation page I linked. It doensn't find all the circles, but some tuning may help
im = imread('http://i.stack.imgur.com/NRwUq.jpg');
%find circles doesn't work well on small images, I made the image
%three times larger, if you have larger images you should use those for
%better results
bim = imresize(im, 3*size(im));
%find and display circles
[centers, radii] = imfindcircles(bim,[8 20],'ObjectPolarity','bright',...
'Sensitivity',0.9);
imshow(bim);
h = viscircles(centers,radii);
number_of_bars = numel(centers)
I added green dots to circles the detector missed and blue X's over incorrect detection. I did these by hand, but the red circles were located by matlab.
I have a problem very similar but very much simple than this.
To begin with I have a small image:
Then I take a screenshot and I want to detect if my small house is in the screenshot.
The problem is that my house can be different in size and slightly different in color.
I've found so far the OpenCV library but it seem quite oversized for my need.
Do you know any simpler library to achieve this task?
Tx
Edit: I've found this about SURF algorithm
Judging by your question, there will be no sheer or skew to your image as it will be on screen, whereas the problem you referenced is a much more difficult situation. Your image will not experience any distortion, but only an increase/decrease in size.
To match regardless of color, I recommend computing the gradient image (using sobel kernels) for both your template image and your screen shot. Now you're matching based on visible edges and take color out of the mix.
To match regardless of size, create multiple versions of your template (the more versions you make the more precise, but the longer the processing) and slide your template across the image until you find an acceptable match.
OpenCV is a beast that has a steep learning curve. If my assumptions are correct, then you are correctly stating that OpenCV is oversized when simple image processing techniques can be applied :).
I am working on images that are partially blur on some sections. These are noises that should be taken care of, but here is the problem:
Are there methods to detect whether an image is blur or partially blur at some sections of an image? For instance, take a look at sample image below:
You can see in the image that there are 3 sections that are visually blur: bottom-left, near center region and top-right. Now, is it possible to detect that any portion of an image is blur programming-wise or mathematically?
As lain_b pointed out, with an image like this you can use an edge detector and look for an absence of edges. I tried it on your image and it seems to work pretty well. First I used the kernel
[0,1,0,
1,-4,1,
0,1,0]
Which is a simple edge detector. Its result was
Then I used a threshold to get
Then I closed the image and opened it to get
This is obviously not a finished version, the top right portion did not recognize well at all. Perhaps you could improve it by blurring before performing thresholding, or by choosing better values for the threshold and the radii of the opening and closing operations. A lot of the decisions you will need to make depend on the constraints you can put on your problem. I think this technique will work for you though.
Edit
If you are looking for blur detection of arbitrary images you are going to have to investigate a wide variety of techniques. Things are much easier if you can make assumptions about your set of input images. Without any assumptions I don't know what will work best for you. Here is some reading on the topic
Image Blur Metrics
Reserach paper on using the Harr wavelet transform
Similar SO Question and look at the question that question links to
Blur detection is a very active research field, there is no one answer. You will just need to try all the methods you can find (these were found by googling detect blur in image).
This paper may be of some help. It does blur estimation (mostly for out of focus, but I think it also does blur) to recreate a similarly blurred object in the image.
I think you should be able to use it to detect the blurred areas, and how blurred they are. It should be especially relevent to your problem as it is designed to work with real-world images.
So I'm using openCV to do square recognition on this image. I compiled the squares.c file on an image that I took and here are the results:
http://www.learntobe.org/urs/index1.php
The image on the left is the original and on the right is the image that is a result of running the square detection.
The results aren't bad, but I really need this to detect ALL of the squares and I'm really new to this openCV and image processing stuff. Does anyone know of how I can edit the squares.c file to possibly get the detection to be more inclusive so that all of the squares are highlighted?
Thanks a lot ahead of time.
All the whitish colors are tough to detect. Nothing separates it from the page itself. Try doing some kind of edge detection (check cvCanny or cvSobel).
You should also "pre-process" the image. That is, increase the contrast, make the colors more saturated, etc.
Also check this article http://www.aishack.in/2010/01/an-introduction-to-contours/ It talks about how the squares.c sample works. Then you'll understand a bit about how to improves the detection in your case.
Hope this helps!