I want to track an object in a video. So i suppose that I could use "Gaussian Mixture Models" in Opencv and C++ . I want to know how to write Gaussian Mixture Models in C++ . Are there any better algorithms for this than GMM?
Sorry to not answer the question directly but:
Reading research papers is a great thing to do, but to be honest, you will get much more knowledge at this point by trying your own ideas on your specific data and getting a better understanding of the problem.
If you know the shapes, it's probably better to use a generalized Hough transform or matched filter for position estimates, combined with a Kalman filter for tracking. These will be relatively easy to implement. Or maybe you can find existing implementations.
Also, I'd prototype your idea in Matlab or Octave instead of C++ if you are not a very good C++ programmer as you'll wind up wasting most of your time with problems in C++ when the problem itself is what you really want to focus on.
As I said in the comment, I'd skip out on using GMM's for now until you get a better understanding of the problem and how you are going to use them. (Unless of course you already have a good idea of how you will use them.)
Related
I have a set of data z(0), z(1), z(2)...,z(n) that I am currently fitting with a 2 variables polynomial of the kind p(x,y) = a(1)*x^2+a(2)*y^2+a(3)*x*y+a(4). I have i=1,...,n (x(i),y(i)) coordinates that I impose to be p(x(i),y(i))=z(i). In this way I have a Overdetermined System that I can solve using Eigen SVD . I am looking for a more sophisticated method that can take care of outliers, like a Least Median of Squares robust regression (as described here) but I haven't found a C++ implementation for 2 variables. I looked in GSL but it seems there is nothing for 2 variable functions. The only other solution I can think of is using a TGraph2D in ROOT. Do you know any other solution? Numerical recipes maybe? Since I am writing C++ code I would prefer C or C++ implementations.
Since non answer has been given yet, but I am still working on this problem, I will share my progresses here.
The class TLinearFitter has a fit method that allows you to select Robust fitting - Least Trimmed Squares regression (LTS):
https://root.cern.ch/root/html532/TLinearFitter.html
Another possible solution, more time consuming maybe, but maybe more efficient on the long run is to write my own function to be minimized, and the use:
https://projects.coin-or.org/Ipopt to minimize it. Although in this approach there is a bigger "step". I don't know how to use the library and I haven't (yet?) found a nice tutorial to understand it.
here: https://wis.kuleuven.be/stat/robust/software there is a Fortran implementation of the LMedS algorithm called PROGRESS. So another possible solution could be to port this software to C/C++ and make a library out of it.
Is there any best approach or template to follow, while doing this?
I mean two things in particular, because for me it is problematic to imagine how it would go in c++:
expanding arrays on go, where they are expanded during program and i dont know whethere the final size will be e.g. 10 or 100000.
plots. I have never done any plot in c++ as I always have been doing it in matlab when necessary.
So what templates or rules should I follow, and how could I cope with those two things?
I found that eigen library would be useful for matrices (dynamically expanding also?), but as I am not sure, want to ask first to be sure of a right approach. Nothing i know about plots.
Please add some link for me to learn from, if useful.
Thanks!
expanding arrays on go, where they are expanded during program and i dont know whethere the final size will be e.g. 10 or 100000.
The solution to this is simple: look up std::vector (or std::deque) both provide this behaviour. (With "subtle" differences between a deque and a vector).
plots. I have never done any plot in c++ as I always have been doing it in matlab when necessary.
For this you'll have to search for a library that can do this, first you'll have to look into a graphical window library such as Qt. And then you'll have to look up some library that can plot data in a graph form.
Though for this matlab will probably always be the "easier/better" choice; C++ has nothing to help you with this.
Also remember: first learn the language, then learn libraries!
For plotting using QT, QWT is basically all you need as it provides all the non trivial kind of charts one may need.
I've been wanting to write my own multithreaded realtime raytracer in C++, but I don't want to implement all the vector and matrix logic that comes with it. I figured I'd do some research to find a good library for this, but I haven't had much success...
It's important that the implementation is fast, and preferably that it comes with some friendly licensing. I've read that boost has basic algebra, but I couldn't find anything on how good it was regarding its speed.
For the rest, Google gave me Armadillo, which claims to be very fast, and compares itself to certain other libraries that I haven't heard of.
Then I got Seldon, which also claims to be efficient and convenient, although I couldn't find out where exactly they are on the scale.
Lastly I read about Eigen, which I've also seen mentioned here on StackOverflow while searching here.
In the CG lecture at my university, they use HLSL for the algebra (making the students implement/optimise parts of the raytracer), which got me thinking whether or not I could use GLSL for this. Again, I have no idea what option is most efficient, or what the general consensus is on algebra libraries. I was hoping SO could help me out here, so I can get started with some real development :)
PS: I tried linking to sites, but I don't have enough rep yet
I'd recommend writing your own routines. When I wrote my raytracer, I found that most of the algebra used the same small collection of methods. Basically all you need is a vector class that supports addition, subtraction, etc. And from there all you really need is Dot and Cross.
And to be honest using GLSL isn't going to give you much more than that anyways (they only support dot, cross and simple vector math, everything else must be hand coded). I'd also recommend prototyping in C++ then moving to CUDA afterwards. It's rather difficult to debug a GPU code, so you can get it working in the CPU then recode it a bit to work in CUDA.
In reality raytracers are fairly simple. It's making them fast that is hard. It's the acceleration structures that are going to take most of your time and optimization. At least it did for me.
You should take look at http://ompf.org/forum/
This forum treats of realtime raytracing, mostly in C++. It will give you pointers, and sample source.
Most of the time, as every cycle count, people do not rely on external vector math libs: optimizations depend on the compiler you're using, inlining, use of SSE (or kindof) or not, etc.
I recommend "IlmBase" that is part of the OpenEXR package. It's well-written C++, developed by ILM, and widely used by people who professionally write and use graphics software.
For my projects I used glm, maybe it would also suit you.
Note that libraries such as boost::ublas or seldon probably won't suit you, because they're BLAS-oriented (and I assume you're looking for a good 3D-driven linear algebra library).
Also, the dxmath DirectX library is pretty good, although sometimes hard to use, because of it's C-compatible style.
You might look at the source code for POVRAY
I just found out about the Boost Phoenix library (hidden in the Spirit project) and as a fan of the functional-programming style (but still an amateur; some small experience with haskell and scheme) i wanted to play around with this library to learn about reasonable applications of this library.
Besides the increasement of expressiveness and clarity of the code using fp-style, i'm especially interested in lazy-evaluation for speeding up computations at low costs.
A small and simple example would be the following:
there is some kind of routing problem (like the tsp), which is using a eucliedean distance matrix. We assume, that some of the values of the distance matrix are never used, and some are used very often (so it isn't a good idea to compute them on the fly for every call). Now it seems to be reasonable to have a lazy data-structure holding the distance values. How would that be possible with phoenix? (ignoring the fact that i would be easily done without fp-style-programming at all) Reading the official documentation of phoenix didn't let me understand enough to answer that.
Is it possible at all? (in Haskell for example the ability to create thunks which are guaranteeing that the value can be computed later are in the core of the language).
What does using a vector with all the lazy functions defined in phoenix mean? As naive as i am, i tried to fill two matrices (vector >) with random values, one with the normal push_back, the other with boost::phoenix::push_back and tried to read out only a small amount of values from these matrices and store them in a container for printing out. The lazy one was alway empty. Am i using phoenix in a wrong way / it should be possible? Or did i misunderstand the function of the containers/algorithms in phoenix. A small clue for the latter one is the existence of a special list-data-structure in the FP++ library, which influenced phoenix.
Additionally:
What are you using phoenix for?
Do you know some good ressources regarding phoenix? (tutorials, blog entries...)
Thanks for all your input!
As requested, my comment (with additions and small modifications) as an answer...
I know your position exactly, I too played around with Phoenix (although I didn't dig in very deeply, mostly a byproduct of reading the Boost::Spirit tutorial) a while ago, relatively soon after catching the functional bug and learning basic Haskell - and I didn't get anything working :( This is btw in synch with my general experience with dark template magic: Very easy to misunderstand, screw up and get punched in the face with totally unexpected behaviour or incomprehensible error messages.
I'd advice you to stay away from Phoenix for a long time. I like FP too, but FP in C++ is even uglier than mutability in Haskell (they'd be head to head but C++ is already ugly and Haskell is, at least according to Larry Wall, the most beautiful language ever ;) ). Learn and use FP, and when you're good at it and forced to use C++, use Phoenix. But for learning, a library that bolts a wholly different paradigm on an already complex language (i.e. FP in C++) is not advisable.
I am newbie for integer linear programming.
I plan to use a integer linear programming solver to solve my combinatorial optimization problem.
I am more familiar with C++/object oriented programming on an IDE.
Now I am using NetBeans with Cygwin to write my applications most of time.
May I ask if there is an easy use ILP solver for me?
Or it depends on the problem I want to solve ? I am trying to do some resources mapping optimization. Please let me know if any further information is required.
Thank you very much, Cassie.
If what you want is linear mixed integer programming, then I would point to Coin-OR (and specifically to the module CBC). It's Free software (as speech)
You can either use it with a specific language, or use C++.
Use C++ if you data requires lots of preprocessing, or if you want to put your hands into the solver (choosing pivot points, column generation, adding cuts and so on...).
Use the integrated language if you want to use the solver as a black box (you're just interested in the result and the problem is easy or classic enough to be solved without tweaking).
But in the tags you mention genetic algorithms and graphs algorithms. Maybe you should start by better defing your problem...
For graphs I like a lot Boost::Graph
I have used lp_solve ( http://lpsolve.sourceforge.net/5.5/ ) on a couple of occasions with success. It is mature, feature rich and is extremely well documented with lots of good advice if your linear programming skills are rusty. The integer linear programming is not a just an add on but is strongly emphasized with this package.
Just noticed that you say you are a 'newbie' at this. Well, then I strongly recommend this package since the documentation is full of examples and gentle tutorials. Other packages I have tried tend to assume a lot of the user.
For large problems, you might look at AMPL, which is an optimization interpreter with many backend solvers available. It runs as a separate process; C++ would be used to write out the input data.
Then you could try various state-of-the-art solvers.
Look into GLPK. Comes with a few examples, and works with a subset of AMPL, although IMHO works best when you stick to C/C++ for model setup. Copes with pretty big models too.
Linear Programming from Wikipedia covers a few different algorithms that you could do some digging into to see which may work best for you. Does that help or were you wanting something more specific?