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/
Related
My goal of a project is to correctly assign medications. I have a large catalog at my disposal for this purpose. However, the medications do not appear there in exactly the same spelling. Possibly additional information was added or possible parts of the prescription were abbreviated.
I was already able to implement a possible algorithm using the Levensthein distance (token_set_ratio).
Because of the sometimes long additional information this algorithm assigns wrong medications, I wanted to ask if there are better algorithms for comparing strings. For example, does it make sense to implement machine learning algorithms or NLP technology? This is a relatively new area for me. I would appreciate any ideas or inspiration.
This sounds like a classic Deduplication task. For example, have a look at dedupe. This tool lets you annotate training examples and learns when two items refer to the same thing. It can be used with as few as 10 training sanples and has an active learning approach implemented.
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.
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.
I have made a videochat, but as usual, a lot of men like to ehm, abuse the service (I leave it up to you to figure the nature of such abuse), which is not something I endorse in any way, nor do most of my users. No, I have not stolen chatroulette.com :-) Frankly, I am half-embarassed to bring this up here, but my question is technical and rather specific:
I want to filter/deny users based on their video content when this content is of offending character, like user flashing his junk on camera. What kind of image comparison algorithm would suit my needs?
I have spent a week or so reading some scientific papers and have become aware of multiple theories and their implementations, such as SIFT, SURF and some of the wavelet based approaches. Each of these has drawbacks and advantages of course. But since the nature of my image comparison is highly specific - to deny service if a certain body part is encountered on video in a range of positions - I am wondering which of the methods will suit me best?
Currently, I lean towards something along the following (Wavelet-based plus something I assume to be some proprietary innovations):
http://grail.cs.washington.edu/projects/query/
With the above, I can simply draw the offending body part, and expect offending content to be considered a match based on a threshold. Then again, I am unsure whether the method is invariable to transformations and if it is, to what kind - the paper isn't really specific on that.
Alternatively, I am thinking that a SURF implementation could do, but I am afraid that it could give me false positives. Can such implementation be trained to recognize/give weight to specific feature?
I am aware that there exist numerous questions on SURF and SIFT here, but most of them are generic in that they usually explain how to "compare" two images. My comparison is feature specific, not generic. I need a method that does not just compare two similar images, but one which can give me a rank/index/weight for a feature (however the method lets me describe it, be it an image itself or something else) being present in an image.
Looks like you need not feature detection, but object recognition, i.e. Viola-Jones method.
Take a look at facedetect.cpp example shipped with OpenCV (also there are several ready-to-use haarcascades: face detector, body detector...). It also uses image features, called Haar Wavelets. You might be interested to use color information, take a look at CamShift algorithm (also available in OpenCV).
This is more about computer vision. You have to recognize objects in your image/video sequence, whatever... for that, you can use a lot of different algorithms (most of them work in the spectral domain, that's why you will have to use a transformation).
In order to be accurate, you will also need a knowledge base or, at least, some descriptors that will define the object.
Try OpenCV, it has some algorithms already implemented (and basic descriptors included).
There are applications/algorithms out there that you can "train" (like neural networks) and are able to identify objects based on the training. Most of them (at least, the good ones) are not very popular and can only be found in research groups specialized in computer vision, object recognition, AI, etc.
Good luck!
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.