Problem:
Atmospheric turbulation problem occurs due to foze,temperature variations, looking at to long distances with camera. There is a problem sample below
Example:
What I tried so far:
When I searched, I found this article which suggests to use
Dual Tree Complex Wavelet Transform(DTCWT). I followed through the
article and it seems like a good way to apply. Then I found this
implementation of the article in python code. I have run it and
seen the results but not as expected well.
I have simply took the average of some frames(let say 10), and get a result. I did it because the shaking pixels will be mostly true ones at any time. My results seems not bad after averaging.
What I need:
After searching much I am sure that this solution needs more frame to get one good frame. To do that I need a good image fusion algorithm.
I kindly request that you can suggest me any algorithm or any approaches I can try and go throuh to solve problem.
Note: I am familiar with OpenCV and C++ so priority is on these.
Related
I am working on a project which determines the indoor position of an object which moves in 3D space (e.g. a quadcopter).
I have built some prototypes which use a combination of gyroscope, accelerometer and compass. However the results were far from being satisfactory, especially related to the moved distance, which I calculated using the accelerometer. Determining the orientation using a fusion of gyroscope and compass was close to perfect.
In my opinion I am missing some more sensors to get some acceptable results. Which additional sensors would I need for my purpose? I was thinking about adding one or more infrared cameras/distance sensors. I have never worked with such sensors and I am not sure which sensor would lead to better results.
I appreciate any suggestions, ideas and experiences.
The distance checking would decidedly help. The whole algorithm of any surface geo survey is based on the conception of start/final check. You know the start, then you add erroneous steps, and come to the finish that you know, too. But you have collected some sum error by the way. Then you distribute the error found among all steps done, with the opposite sign, of course.
What is interesting, in most cases you not only somewhat diminish the effect of arbitrary mistakes, but almost eliminate the systematical ones. Because they mostly are linear or close to linear and such linear distribution of found error will simply kill them.
That is only the illustration idea. Any non-primitive task will contain collecting all data and finding their dependencies, linearizing them and creating parametrical or correlational systems of equations. The solving of them you get the optimal changes in the measured values. By parametrical method you can also easily find approximate errors of these new values.
The utmost base of these methods is the lesser squares method of Gauss. The more concrete methodics can be found in old books on geodesy/geomatic/triangulation/ geodesy nets. The books after introduction of GPS are for nothing, because everything was terribly simplified by it. Look for the books with matrix formulaes for lesser squares solutions.
Sorry if I had translated some terms into English with errors.
In an effort to improve the image quality I'm getting from a camera I started digging up alternative demosaicing technics. I found out that there are allot of different algorithms but most of them are dated back to 2000. Since more than a decade has passed, I was wondering what are the latest technics around?
Algorithm or source code would be highly appreciated.
It's not the most recent work, but the results are a good improvement over classic algorithms: http://www.ipol.im/pub/algo/bcms_self_similarity_driven_demosaicking/
And you can explore the site for more algorithms and examples, with online demos and source code (open source licences). It should help you getting started.
Not latest, published in 2007, the demosaicing with DFPD (Directional Filtering and a posteriori Decision) seems to give the best result. Not only it removes the artifacts at high frequency regions, it produces more crisp and natural colors than previous approaches.
This blog post compares the DFPD approach with hq bilinear interpolation and the result is quite obvious: DFPD reconstructs the image with better colors and without artifacts.
I'm building a rhythm-based game, and facing a lot of problems with rhythm-detection. I receive the current spectrum of a playing song. It looks like a float array with 512 floats. 256 for left and right channel representation. FFT is also available. But I have no idea how to work with that data, I've made some experiments with visualizing, but it gave me very few information. I've googled for some ready algorithms, but there is nothing. Please, can someone help me with, maybe, some references, materials, articles connected with rhythm detection, working with audio spectrum. Code will also be very helpful. Thanks.
Maybe you didn't use the right search-terms. Try to google 'tempo detection' or 'beat detection', together with 'code' or 'algorithm'. There are lots of papers, references, code examples, etc.
Just a few hits:
http://www.cs.princeton.edu/~lieber/cos325/final/
http://www.clear.rice.edu/elec301/Projects01/beat_sync/beatalgo.html
You might want to check out the source and project report for the Dancing Monkeys project. Dancing monkeys automatically generates stepfiles for DDR, and it does so using some rather sophisticated beat detection. It's written in matlab.
You should have a look at the beat spectrum algorithm: http://www.rotorbrain.com/foote/papers/icme2001/icmehtml.htm.
It extracts information about rythm and musical structure by computing the similarity of small samples' spectrograms. It is relatively easy to implement and allows robust information to be retrieved.
I am a beginner in image processing. I want to write an application in C++ or in C# for
Searching an image in a list of images
Searching for a particular feature (for e.g. face) in a list of images.
Can anybody suggest where should I start from?
What all should I learn before doing this?
Where can I find the correct information regarding this?
In terms of the second one, you should start off with learning how to solve the decision problem of whether a square patch contains a face (or whatever kind of object you are interested in). For that, I suggest you study a little bit of machine learning, the AdaBoost algorithm, Haar features, and Viola-Jones.
Once you know how to do that, the trick is really just to take a sliding window across your image, feeding the contents of that window into your detector. Then you shrink your main input image and repeat the process until your input image has gotten smaller than the minimum size input for your detector. There are, of course, several clever ways to parallelize the computation and speed it up, but the binary detector is really the interesting part of the process.
You may find some of the material linked from the CSE 517: Machine Learning - Syllabus helpful in getting into machine learning and understanding AdaBoost. You will certainly find the Viola-Jones paper of interest.
I want to write an algorithm that can take parts of a picture and match them to another picture of the same object.
For example, If I gave the computer a picture of a vase and a picture of a scene with the vase in it, I'd expect it to determine where in the image the vase is.
How would I begin to develop an algorithm like this?
The final usage for this algorithm will be an application that for example with a picture of somebody's face could tell if they were in a crowd of people. This algorithm would eventually be applied to video streams.
edit: I'm not expecting an actual solution to this problem as I don't hope to solve it anytime soon. The real question was how do you define something like this to a computer so that you could make an algorithm to do it.
Thanks
A former teacher of mine wrote his doctorate thesis on a similar sort of problem, except his input was a detailed 3D model of something, which he would use to find that object in 2D images. This is a VERY non-trivial problem, there is no single 'answer', certainly nothing that would fit the Stack Overflow format.
My best answer: gather a ton of money and hire a very experienced programmer.
Best of luck to you.
The first problem you describe and the second are both quite different.
A major part of each is solved by the numerous machine vision libraries available. You may need a combination of techniques to achieve any success at either task.
In the first one, you would need something that generically recognizes objects. Probably i'd use a number of algorithms in concert to identify the foreground object in the model image and then do some kind of weighted comparison of the partitioned target image.
In the second case, examining faces, is a much more difficult problem relative to the general recognizer above. Faces all look the same, or nearly so. The things that a general recognizer would notice aren't likely to be good for differentiating faces. You need an algorithm already tuned to facial recognition. Fortunately this is a rapidly maturing field and you can probably do this as well as the first case, but with a different set of functions.
The simple answer is, find a mathematical way to describe faces, that can account for angles and partial missing data, then refine and teach it.
Apparently apple has done something like this, however, it still makes mistakes and has to be taught as it moves forward.
I expect it will be more about the math, than about the programming.
I think you will find this to be quite a challenge. This is an extremely difficult problem and is one of the many areas of computing that fall under the domain of artificial intelligence (AI). Facial recognition would certainly be the most popular variant of this problem and in spite of what you may read in the media, any claimed success are not what they are made out to be. I think the closest solutions involve neural nets and they require very clear and carefully selected images usually.
You could try reading here though. Good luck!