all the pattern-recognition related posts here are dealing with face-, hand-, characters-recognition.
I wonder, is someone has successivly used OpenCV for recognizing cells on a picture from a microscope.
What I am currently able to do, is counting cells using threshold and shape-detection (change threshold, count shapes, discard shapes with invalide size).
My next task is recognizing among about 20 types of cells. It would be very interesting to exchange the experience. May be, the OpenCV is not the proper/overweighted tool for this?
Regards,
Valentin Heinitz
EDIT
An up-vote has drown my attention to this old question again. I completed the task finally with OpenCV. It works good and the tool was approved by FDA last year as a software part of a diagnostic device :-)
Now I think OpenCV was the perfect tool for this, despite I had to implement Haralick-Features myself.
your problem is a machine learning problem. OpenCV offer a few tools for that, SVM would be a good fit for what you are trying to do. I have experience with Kohonen neural networks, which would be a good idea too, if you have a good database of your cells. However, I do not know how good you are in C++, but it is always a good idea to start with matlab, get your algorithm to work, and then rewrite it in C++. To test your ideas quickly, matlab is definitely a better tool, with a lot of ML functions availables.
Related
I am trying to Code a genetic algorithm in Matlab but really dont know how it works in images and how to proceed? Is there any basic tutorial that can help me understand how to apply GA on images (starting from 2d to multidimentional images ).
That will be a great help for me.
Thanking everyone in anticipations.
Kind Regards.
For GA you need two things: a fitness function that can evaluate any solution and tell how good it is, and a representation of your solution so that you can do crossover and mutation. Once you have these, you are good to go. I'm not an expert on image processing so I can't help you with that exactly.
Look at the book Essentials of metaheuristics which is a very good resource for start with evolutionary computation (and not only that) in general. It's free.
There is a paper on this subject which you can find at the IEEE library. I believe it solves the problem you vaguely describe.
Can the Hough Transform be used in commercial software?
I mean, it is one of those things that seem research only and unstable.
You would not put it in a commercial compositing software for example
and have the user rely on it at all times.
Any opinions?
Thanks
The Hough transform has been in use in commercial and industrial applications all over the world for years, decades even. From the wikipedia page you can see that it was first developed in 1972, based on earlier ideas from 1962. That means it is older than the CCD that you use to capture the images you use in the compositing software.
Given that it "seems research only and unstable" to you, I would suggest you spend some time learning various computer vision and image analysis algorithms and techniques, and get a good mathematical basis in the field in general before you implement the Hough transform in commercial compositing software.
And when you are done studying I'd suggest you use a well tested open source implementation.
Yes. In fact, I've written Hough transform code for a piece of commercial software that wasn't meant to be a research tool like MATLAB. Though I put a lot of time into its robustness towards a specific application, it worked great.
The Hough transform by itself can sometimes be unreliable in applications where you have some level noise, such in webcams, or when there are some distortions in the shape you need to extract. This may be what you are seeing. In this case you may need to do a little more tuning towards your application, or try some basic image preprocessing.
I'm a bit annoyed with the condescending tone in both the comment to the question (by High Performance Mark), as well as the accepted answer here.
Firstly, that programming libraries/frameworks provide an implementation of an algorithm does not mean it is used, or rather, suited for commercial applications (i.e. getting the job done, robustly, on less pristine conditions). The Hough transform is a well defined algorithm (with possible uses and limitations) which is simple enough to understand, and very commonly taught in introductory image processing courses. Not surprisingly, it has been implemented in general purpose libraries such as Matlab's, Octave's and OpenCV. I don't believe the question was intended to discuss the robustness of an implementation and possibility of inclusion in commercial image processing frameworks, but rather if the algorithm itself is well suited for end user software (an application that counts circles, or what not).
The accepted answer, as it stands, is "The algorithm is very old. Here is a book on image processing, here is a link to a image processing library that has implemented it". The other answer with zero score seems to be on topic (i.e. discussion possible applications), though isn't very specific ("worked for me").
So, why do some people get the impression that the hough transform is unreliable for shape detection? Here is a good example: Unreliable results with cv2.HoughCircles
The input seems to be very well defined circles. However, the more robust, suggested working solution doesn't use Hough transform. I've had similar experience with my own projects. Usually, the more robust way is some kind of object segmentation, distance transform, watershed and peak localization. Have I ever used Hough transform with good results? No. I think it could be useful in some cases. In particular if the shapes of the imaged objects are perfectly defined, and partially occluded.
In other words, I'm also curious as to commercial applications that ended up benefiting from Hough transform. That's how I came across this question, and subsequently disappointed in the "you wouldn't ask that question if you understood the subject better", responses.
I'm trying to cluster a really large dataset - 3030764x162 into 4000 clusters using the cvKmeans2 function in OpenCV 2.1.
I would like to see which iteration the K-means algorithm is currently in (similar to what is displayed in Matlab), but I don't see any documentation that points to how I can do this.
It's kind of frustrating seeing a blank screen and not knowing when the code is going to terminate!
Thank you.
Unfortunate as it seems, the answer is No, you cannot. There are no debugging/informative statements anywhere in the kmeans function as provided by OpenCV. However, you may edit and add statements to the method as you deem appropriate.
#Sau,
May be you need some other way of doing it. Though my answer is not relevant to OpenCV.
I have not tried in OpenCV, I had once done KMeans clustering for a extremely large data set and it was more a option better than OpenCV as it worked in a distributed mode. Though very lengthy, but still you might be interested. Its Kmeans clustering using Mahout
Check it out
I want to detect one kind of object such as person in the picture,
who can tell me how to training a kind of people classifier for use,so we can use the classifier to detect people in any picture.
It's a pretty vague question but i think you're looking for a good computer vision library. The gold standard is OpenCV (Open Source Computer Vision). That'll get you started, and there's lots of people that have done facial recognition with it.
If you want to tell two people apart, that's a hugely more complicated problem. You'll likely use some of the same tools, but you'll need much more complicated algos.
you can take a look at viola-jones framework: Viola-Jones Object Detection Framework at Wikipedia
it's cvHaarDetectObjects() in OpenCV.
I'm new to image processing and I want to do a project in object detection. So help me by suggesting a step-by-step procedure to this project. Thanx.
Object detection is a very complex problem that includes some real hardcore math and long tuning of parameters to the computation methods involved. Your best bet is to use some freely available library for that - Google will help.
There are lot of algorithms about the theme and no one is the best of all. It's usually a mixture of them what makes the best solution to the solution.
For example, for object movement detection you could look at frame differencing and misture of gaussians.
Also, it's very dependent of your application, the environment (i.e. noise, signal quality), the processing capacity you may have available, the allowable error margin...
Besides, for it to work, most of time it's first necessary to do some kind of image processing to the input data like median filter, sobel filter, contrast enhancement and a large so on.
I think you should start reading all you can: books, google and, very important, a lot of papers about the subjects (there are many free in internet) you are interested in.
And first of all, i think it's fundamental (at least it has been for me) having a good library for testing. The one i have used/use is OpenCV. It's very complete, implement many of the actual more advanced algorithms, is very active, has a big community and it's free.
Open Computer Vision Library (OpenCV)
Have luck ;)
Take a look at AForge.NET. It's nowhere near Project Natal's levels of accuracy or usefulness, but it does give you the tools to learn the algorithms easily. It's an image processing and AI library and there are several tutorials on colored object tracking and motion detection.
Another one to look at is OpenCV from Intel. I believe it's a bit more advanced, but it's written in C.
Take a look at this. It might get you started in this complex field. The algorithm pages that it links to are interesting reading.
http://sun-valley.stanford.edu/projects/helicopters/final.html
This lecture by Jeff Hawkins, will give you an idea about the state of the art in this super-difficult field.
Seems that video disappeared... but this vid should cover similar ground.