Develop Reference

C++ Triangulation library in a Java Application

I'm implementing an algorithm for pathfinding that is a variant of A* (HPA*) but with a triangulated search space. I've been making a Java application and have written a good amount of code, but I recently found a C++ library that already takes care of the triangulations for the project. I have several options but I'm not sure what I should do: convert the library to Java or integrate the C++ code into my Java library. I could also rewrite my code in C++ but I'm not very familiar with the language.
This application tests the performance of an algorithm for a paper I'm writing so it doesn't need to be portable. What do you think my best option is?

If you really want to call C++ code from Java, read up on the Java Native Interface (JNI) coding conventions.
But generally, unless you have an extremely good reason to cross languages, it's more trouble than it's worth. How complicated is the triangulation code? If it's less than a few hundred lines, I'd suggest reimplementing; if it's more than a few thousand, using a library may be justified but you should probably check whether there's a Java library available or if someone has already written the appropriate JNI glue code.

How does LIUM Speaker Diaritization work?

in my project, I am using the library called LIUM_SpkDiarization-4.7.jar, but I am not quite sure how does it work. Could anyone, please, explain it a bit?
Also, I'm using it with python.
The link to the library is: https://voiceid.googlecode.com/svn-history/r11/trunk/scripts/LIUM_SpkDiarization-4.7.jar
Thanks in advance.

I was not aware of this tool. It looks really cool. Have you checked their wiki? They have some papers on how the system works: http://lium3.univ-lemans.fr/diarization/doku.php
Basically, they compute MFCC Mel Frequency Cepstrum Coefficients (standard technique). This is the fundamental step. It generates a feature space to work with. It is similar to computing FFT over sliding window in time. Ultimately clustering is performed on these time sliced features using Bayesian Information Criterion (BIC) methods. First to segment the time based feature space, then to cluster, and find consistent features for each speaker. HMM, viterbi, EM, and sometimes GMM can be used as well.
I don't know the algorithm well enough to explain it in detail, but this should also help: http://lium3.univ-lemans.fr/diarization/doku.php/overview

Computer vision, C++ OR Java

I'm a complete newbie in the computer vision world and recently I implemented some examples using OpenCV with Java. I'm impressed with the potential this area has and wish to continue learning more.
I learned that OpenCV is written in C++ and while Java has a wrapper (JavaCV) I understood that the applications in Java are slower than in C++ and most enterprise application are written in C++.
My question is this: I have few years of experience in Java and I feel very comfortable to write any application with it; would it be smart to move to C++ to learn CV or should I stick with Java and use its wrapper.

Computer vision is a demanding area - and while it is true that you'd best stay with what you know, and move to opencv only if performance is needed, another truth is that you'll need to go deep into mathematics, pointers and algorithms to learn and build a good computer vision app. And to do that in Java can be more cumbersome than learning c++.
So, if all you want to do is to apply a filter over an image for some school project - go for Java. But if you want to stay more with OpenCV, to learn vision algorithms or to write your own, my strong suggestion is to learn C++ - isn't that scary!
A reason to write native code is flexibility - you'll want to do all kind of tricks that are difficult or performance-killers in Java.
Shortly speaking, learning C++ is much simpler than computer vision. And OpenCV is not just a library you want to call to do some processing out there. It's bleeding edge technology - you'll want to understand it, to hack into it, to build over it, to go through the code, much more than call someJNIfunc();
And if you do so, please be aware that OpenCV offers two interfaces - one for C and one for C++. And while they offer the same functionality, the C++ one is much like Java - with automatic memory management and more sweeties. You can refer to this post to see the differences

I suggest sticking with what you're comfortable with for now. Only switch to C++ when you find that it doesn't expose certain APIs you want or performance actually becomes a problem. Right now, you're in the learning phase.

JavaCV uses a wrapper called JavaCPP to call OpenCV from Java programs. JavaCPP automatically generates temporary native libraries that form a bridge used by JNI to let Java programs call the OpenCV native libraries.
The solution is elegant and it works well, but is quite finicky about installing just the right versions and having classpaths set correctly. You can get a glimpse of the difficulties people face at the JavaCV discussion forum, and at http://code.google.com/p/javacv/wiki/Windows7AndOpenCV.
I got this working with OpenCV-2.3.1 on XP, Windows 7, and Ubuntu 10.11, and still it took me several days to get it all updated to OpenCV-2.4.2. ffmpeg is especially tricky to get right across all platforms.
There is little or no speed overhead if you are using Java for high level program control because image objects and list-based data structures are maintained on the native side through pointers. One pitfall is knowing who is responsible for releasing allocated memory, so be prepared for VM crashes with complicated programs.
There is a bit overhead in transferring data objects to the Java side. I find that it takes about 1 microsecond to copy a keypoint location into a Java-side Point object. This doesn't sound like much but in a real-time application with thousands of keypoints it can make a difference. We also copy JavaCV IplImage objects to Java through ByteBuffers. This takes a millisecond or less so is quite feasible for real-time use.
In our case, we have a substantial body of Java code to leverage against OpenCV. And Java's garbage collection makes many things much much easier. I are satisfied that the overhead of learning JavaCV has been well worth it.
I found it necessary to build the project in Eclipse and compile JavaCV from source instead of using javacv.jar. (You'll need the other .jar files though.) This lets you examine exceptions to track down library version and classpath errors. And the JavaCV source is needed to understand how JavaCV exposes the OpenCV API.

How much time is spent in the OpenCV library and how much time is spend in your program? If your program entirely in C++ it cannot reduce the time spent in your program (outside the library) to less than nothing. e.g. if you spend 99% of your time in the library, using C++ cannot make it more than 1% faster.

For simple programs, Java and C++ doesn't make any significant difference in speed. But for a large code with lot of computational complexity, C++ turns out to be faster.
Wrappers do have a problem of overhead. But this will be negligible for a small program. If you write a complex code in Java, it wont be easy to rewrite it into C++. This is because of the large no of functions available in Java which will be so much different from C++.
Whether you should use Java or C++ for OpenCV depends on your motive.
If you seriously want to lean OpenCV and work on some big projects , I suggest you to move on to C++. But if you are looking for having just some fun with OpenCV, it will be better to stick to what you know.

Among C++ or Java, better use C++. I have almost no experience in Java, but the reason I would recommend C++ is its common usage among ML&CV libraries.
One of the best solution of powerful and flexible computer vision application may be next sequence:
training a model using flexible Python and popular machine learning libraries;
storing pre-trained weights;
rewriting the best model architechture to C++ using OpenCV library with useful, but not extensive Mat class or compiling to C++ ML model;
compiling with definite specification of your device processor.
Advances of this solution are application work speed, code safety, development speed and compatibility with many devices.
Moreover, Python Tensorflow and other models could be loaded to C++, e.g. PyTorch.
Besides, it is much more easier to get help from CV community with the C++ OpenCV code.
Disadvantages are tons of development problems and development speed (which may not be omitted with Java), sometimes strictful flexibility limits of OpenCV lib, and many others you may not think of at the beginning.

HPC (mainly on Java)

I'm looking for some way of using the number-crunching ability of a GPU (with Java perhaps?) in addition to using the multiple cores that the target machine has. I will be working on implementing (at present) the A* Algorithm but in the future I hope to replace it with a Genetic Algorithm of sorts. I've looked at Project Fortress but as I'm building my GUI in JavaFX, I'd prefer not to stray too far from a JVM.
Of course, should no feasible solution be available, I will migrate to the easiest solution to implement.

If you're interested in HPC with GPUs then perhaps you can look jCuda. This provides Java bindings for CUDA, along with access to CUDA FFT, CUDA BLAS and CUDA DPP. I haven't seen any performance reports on this library so I can't guarantee it will be very good.
Beyond that, I'm not really sure. If you're interested in doing this type of stuff as an educational exercise then Java should be good enough, but if you have a serious need for HPC then you're probably going to want to implement in C and use the Java Native Interface to communicate with it.

Morten Nobel Joergensen has a blog post showing how to create a Mandelbrot Set using JOGL - Java Bindings for OpenGL
However if you want generic computing, rather than graphics, then you'd be after the Java bindings for OpenCL, from which you can chose from JOCL, or JOCL or JavaCL.
Wikipedia's page shows how OpenCL can be used to compute a fast fourier transform.

Parallel Colt might be of interest.

Have a look at JPPF, it is a very nice and mature open source Java grid computing environment

Embedding dendrogram in Java

I'm looking for a library capable of drawing dendrograms of data in Java (not calculating them, I can do it by myself).. do you have any clues? Already tried to search it over Google but haven't found anything that is not stand-alone (while I need to embed the generation inside my program).
Thanks!

Check out the JUNG graph library. It won't perform the actual clustering for you but is a really good library for visualising your results.

Take a look at Archaeopteryx. It has fairly many features; it's open source, and it is available in a pre-packaged jar file.
BTW, I use JUNG and really like it. It can perform various clusterings, but AFAIK, it has no inherent dendrogram capabilities. Because it has graphing capabilities, you could roll your own dendrogram, but it would take some work.