zvone's profile - activity

Yeah, I know that, but don't understand this piece of code. For example, why are winSize.width and winSize.height divided by 3?

I got the book. Thanks for recommendation. Still don't understand from which of these 1000 positives are LBP features extracted from?

I am trying to understand what is train_cascade ( using LBP) actually doing with samples. If I have, for example, 1000 positives and 1000 negatives, it calculates LBP histogram for each, right? And what does it do with all of these histograms? From which one does it choose features to make weak learners?

When I train my classifier using opencv_traincascade by using local binary pattern (LBP), I get this written on console :

Number of unique features given windowSize [50,28] : 51408

How is this number calculated? I found code on GitHub, but did not manage to understand it :

void CvLBPEvaluator::generateFeatures()
{
    int offset = winSize.width + 1;
    for( int x = 0; x < winSize.width; x++ )
        for( int y = 0; y < winSize.height; y++ )
            for( int w = 1; w <= winSize.width / 3; w++ )
                for( int h = 1; h <= winSize.height / 3; h++ )
                    if ( (x+3*w <= winSize.width) && (y+3*h <= winSize.height) )
                         features.push_back( Feature(offset, x, y, w, h ) );
    numFeatures = (int)features.size();
}

I am trying to undrestand conceptually how does Cascade Classifier training works in OpenCV using LBP. I understand that AdaBoost is used for choosing weak classifiers and combining them to make a strong classifier. I also understand that LBP is used as visual descriotor of features. But the thing I could not find out is how are weak classifiers actually created from LBP? Is there any learning algorithm used, like Support vector machine? Thanks for help.