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2013-07-05 07:56:14 -0500 asked a question Cluster Points using kmeans

How can I cluster Points using OpenCV's kmeans method? (as in the image, i want to group my points and get their cluster centroids)

image description

2013-05-16 08:44:18 -0500 received badge  Nice Question (source)
2013-04-27 01:38:38 -0500 asked a question How to Store Histogram Values using SparseMat

Calculating and storing the dense histogram matrix for my image processing operations is inefficient. ( there is no many differences on the image's color ) For the sake of efficiency, I want to use a sparse matrix. I could not find any example that stores histogram values on sparse matrix.

How can I use a SparseMat for storing histogram values using OpenCV? Please, can you give me a code example or link that I can read?

2013-02-27 01:55:39 -0500 received badge  Student (source)
2013-02-26 23:52:02 -0500 asked a question How to display image on JAVA release

Hi everyone,

In new release OpenCV 2.4.4, how can I display image ( Mat ) on window using JAVA. I couldn't find the JAVA method from API?

2013-02-08 13:44:58 -0500 commented answer How to speed up this code implemented OpenCV

thank you, but in here calculation of a is not an issue. I asked question wrongly. how can handle like double or triple for?

2013-02-08 13:41:59 -0500 received badge  Editor (source)
2013-02-08 13:19:50 -0500 asked a question How to speed up this code implemented OpenCV

I need an advice for sample below code that requires lots of time for processing. I am developing project on OpenCV and have code blocks like this ( some of them are pictures ). What should I use for more speed? Like, OpenMP or TBB ( that's new in OpenCV and more complex, maybe some examples more helpful ) or GPU ( implementing entire project ) or Boost library or another I don't know 3rd party libraries.

i didn't write multithread on c++ before

thanks for helping now

sample code snippet:

for( int i = 0; i < 3000; i++ )
    for( int j = 0; j < 3000; j++ )
        for ( int index = 0; index < 3000; index++ )
           // float point operations