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.
Related
Can anyone suggest me a good source to learn?
I am a newbie in ML
As I am a newbie, I have not done anything in this.
This might be an excellent place to start. You can create a new kernel straight from the dataset page, and the data will be ready for you when you enter the kernel. You can also look at other people's kernels who have used that dataset, and I bet you'll find plenty of helpful examples.
You'll get lots of hate for asking this kind of question, since it doesn't fit in S.O. question parameters, but I prefer to be a useful human.
I'm trying to use machine learning algorithms for repetitive form filling.
Here is a picture to illustrate that a little bit.
If you enter values in field A and B i would like to have a suggestion for field C.
For this case i really would like to implement a Machine learning algorithm so that the system stays really flexible and only makes suggestions by the knowledge that was build.
I've already started reading programming collective intelligence and Artificial intelligence a modern approach. I also started to play around with Weka a little bit and found a pretty good microsoft research paper on my problem too. But my main problem is that I can't really identify what algorithm group I should use. I'm primarily looking at Descision trees like C 4.5 but I'm not sure if this is the right way. Could you please give me any suggestions on my problem?
It looks like you're starting out... good luck.
Go for a Huffman tree / Genetic algorithm randomizer... for a quick solution.
Go for implementing everything you can think of, then using an external efficacy classifier to figure out what to use for the next iteration, and randomize something along the way.... for the more complex solution.
Decision trees are incredibly inflexible when it comes to this type of stuff. Try fuzzy logic algorithms.
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.
The stereo matching problem consists of obtaining a correspondence between right and left images. I want to do an evaluation between a baseline and a Dynamic Programming method. However, I don't have a baseline yet. I would like to know which method should I use. I was thinking to try a brute-force algorithm. Is there something like that in the literature?
What do you suggest as a baseline method? I want a simple solution, something without heuristics and optimizations, such as this brute-force strategy . But, I have no material to research about it, only methods using Graph Cut, Dynamic Programming etc.
Thanks in advance!
The Middlebury Univ. reference datasets and database of algorithms are the standard everyone uses for evaluation these days.
http://vision.middlebury.edu/stereo/
You should have a look at the basics before delving into graph cuts. Consult the relevant chapter here, it might help http://szeliski.org/Book/
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.