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2015-10-13 16:51:16 -0500 commented answer Comparing 2 LBPs

Great answer. Implemented this and getting great results.

2015-10-13 16:51:09 -0500 commented answer Comparing 2 LBPs

Great answer. Implemented this and getting great results.

2015-10-13 16:50:38 -0500 received badge  Scholar (source)
2015-10-12 11:25:13 -0500 asked a question Comparing 2 LBPs

I am using code from to generate my LBPs. If I generate 2 LBPs and their corresponding histograms, what is the best way to compare them?

This is my code so far:

#include "lbp.hpp"
#include "histogram.hpp"

#include <opencv2/opencv.hpp>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;

int main()
    //template image
    Mat temp = imread("Template.jpg",1);
    //image to be compared to
    Mat match = imread("Match.jpg",1);

    Mat dst,dst2; // image after preprocessing
    Mat lbp,lbp2; // lbp image
    Mat hist,hist2;

    //Convert to gray
    cvtColor(temp, dst, 6);
    cvtColor(match, dst2, 6);
    //remove noise
    GaussianBlur(dst, dst, Size(5,5), 5, 3, BORDER_CONSTANT);
    GaussianBlur(dst2, dst2, Size(5,5), 5, 3, BORDER_CONSTANT);
    //gets the lbp

   // normalize(lbp2, lbp2, 0, 255, NORM_MINMAX, CV_8UC1);
    //normalize(lbp, lbp, 0, 255, NORM_MINMAX, CV_8UC1);

    //get histograms

    //comparing the 2 LBP histograms
    double compareHist = cv::norm(hist-hist2);


    return 0;

Basically it gives me a quantifiable number as to how similar these two images are. My question is, how do I improve this result? Whats a better way of acheiving a quantifiable number based on how similar 2 LBPs are? Or is the way I am doing it even right?


2015-10-04 11:16:24 -0500 asked a question Implementing Local Binary Patterns (C++)

Hi everyone,

I am currently following a tutorial on local binary patterns, however the tutorial is in Python and I have to use C++ (with OpenCV).

The tutorial is about Texture Matching using Local Binary Patterns. Given an image, it tells how similar the texture is to a template. The tutorial can be found here (python):

I have done an implementation so far: LBP.cpp

#include "lbp.hpp"

using namespace cv;

template <typename _Tp>
void lbp::OLBP_(const Mat& src, Mat& dst) {
    dst = Mat::zeros(src.rows-2, src.cols-2, CV_8UC1);
    for(int i=1;i<src.rows-1;i++) {
        for(int j=1;j<src.cols-1;j++) {
            _Tp center =<_Tp>(i,j);
            unsigned char code = 0;
            code |= (<_Tp>(i-1,j-1) > center) << 7;
            code |= (<_Tp>(i-1,j) > center) << 6;
            code |= (<_Tp>(i-1,j+1) > center) << 5;
            code |= (<_Tp>(i,j+1) > center) << 4;
            code |= (<_Tp>(i+1,j+1) > center) << 3;
            code |= (<_Tp>(i+1,j) > center) << 2;
            code |= (<_Tp>(i+1,j-1) > center) << 1;
            code |= (<_Tp>(i,j-1) > center) << 0;
  <unsigned char>(i-1,j-1) = code;

template <typename _Tp>
void lbp::ELBP_(const Mat& src, Mat& dst, int radius, int neighbors) {
    neighbors = max(min(neighbors,31),1); // set bounds...
    // Note: alternatively you can switch to the new OpenCV Mat_
    // type system to define an unsigned int matrix... I am probably
    // mistaken here, but I didn't see an unsigned int representation
    // in OpenCV's classic typesystem...
    dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
    for(int n=0; n<neighbors; n++) {
        // sample points
        float x = static_cast<float>(radius) * cos(2.0*M_PI*n/static_cast<float>(neighbors));
        float y = static_cast<float>(radius) * -sin(2.0*M_PI*n/static_cast<float>(neighbors));
        // relative indices
        int fx = static_cast<int>(floor(x));
        int fy = static_cast<int>(floor(y));
        int cx = static_cast<int>(ceil(x));
        int cy = static_cast<int>(ceil(y));
        // fractional part
        float ty = y - fy;
        float tx = x - fx;
        // set interpolation weights
        float w1 = (1 - tx) * (1 - ty);
        float w2 =      tx  * (1 - ty);
        float w3 = (1 - tx) *      ty;
        float w4 =      tx  *      ty;
        // iterate through your data
        for(int i=radius; i < src.rows-radius;i++) {
            for(int j=radius;j < src.cols-radius;j++) {
                float t = w1*<_Tp>(i+fy,j+fx) + w2*<_Tp>(i+fy,j+cx) + w3*<_Tp>(i+cy,j+fx) + w4*<_Tp>(i+cy,j+cx);
                // we are dealing with floating point precision, so add some little tolerance
      <unsigned int>(i-radius,j-radius) += ((t ><_Tp>(i,j)) && (abs(<_Tp>(i,j)) > std::numeric_limits<float>::epsilon())) << n;

template <typename _Tp>
void lbp::VARLBP_(const Mat& src, Mat& dst, int radius, int neighbors) {
    max(min(neighbors,31),1); // set bounds
    dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32FC1); //! result
    // allocate some memory for temporary on-line variance calculations
    Mat _mean = Mat::zeros(src ...
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2015-10-03 12:02:52 -0500 commented question Texture Matching using LBP/H? C++

@theodore there are some coins without text or numbers. So I need to do texture matching.

2015-10-03 11:34:52 -0500 asked a question Texture Matching using LBP/H? C++

I have a question about LBPs, but firstly let me explain what I am trying to do.

I am trying to match coins using LBPs. I have an input coin and I want to compare it with other coins and see if the coin is of the same denomination. Attached below are 3 images:

  • R1 - the input image - the one to be compared to
  • R1T - needs to be compared to R1
  • R2T - needs to be compared to R1

I have found a very good source here: Local Binary Patterns

All my code needs to simply do is say that R1T is a match and R2T is not a match.

How can I implement something like this?


Images: Thumbnails

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2015-09-21 07:30:14 -0500 commented answer Template Matching for Coins

Thank you for the articles. I will have a look at them. I have updated my question by adding a few images. Maybe to clarify in what I am asking.

2015-09-21 07:27:52 -0500 received badge  Editor (source)
2015-09-21 04:34:48 -0500 asked a question Template Matching for Coins

I am undertaking a project that will automatically count values of coins from an input image. So far I have segmented the coins using some pre-processing with edge detection and using the Hough-Transform.

My question is how do I proceed from here? I need to do some template matching on the segmented images based on some previously stored features. How can I go about doing this.

I have also read about something called K-Nearest Neighbours and I feel it is something I should be using. But I am not too sure how to go about using it.

Research articles I have followed:

2015-09-20 04:01:14 -0500 commented question Matching HOG Images with OpenCV in C++

@LorenaGdL basically I want to input an image with various different coins and I want it to identify each coin correctly and perhaps give a total value of the coins on screen. Basically I've segmented each coin using a hough transform, however how do I say that that coin (segmented) is actually a 10c or 50c etc?

2015-09-12 16:06:21 -0500 commented question Matching HOG Images with OpenCV in C++

@LorenaGdL What would you suggest instead of HOG?

2015-09-10 00:46:46 -0500 asked a question Matching HOG Images with OpenCV in C++

I have currently implemented a way to calculate HOG descriptors using cv::HOGDescriptor() . I have done this for two images and calculated a similarity index between the two images using a basic distance measure.

A good match is something I define as a similarity value below 50. A perfect match has a similarity value of 0

However, when comparing these two almost identical images, I get confusing results (i.e. a high value). Below attached is my code and the 2 images.

Can anyone please tell me what is going wrong? i.e. Why is there a high similarity value when the images are very similar.

    #include <iostream>
    #include <opencv2/core/core.hpp>
    #include <opencv2/imgproc/imgproc.hpp>
    #include <math.h>
    #include "opencv2/ocl/ocl.hpp"
    #include "opencv2/highgui/highgui.hpp"

using namespace std;
using namespace cv;

int main()
   Mat image(imread("R2.jpg",1));
   resize(image, image, Size(64,128) );
   Mat img;
   cvtColor(image, img, CV_RGB2GRAY);

   Mat image2(imread("R2-roi.jpg",1));
   resize(image2, image2, Size(64,128) );
   Mat img2;
   cvtColor(image2, img2, CV_RGB2GRAY);

   vector<float> features;
   vector<Point> locations;
   vector<float> features2;
   vector<Point> locations2;

   HOGDescriptor *hog = new HOGDescriptor();
   HOGDescriptor *hog2 = new HOGDescriptor();

   hog->compute(img,features,Size(32,32), Size(0,0),locations);

   hog2->compute(img2,features2,Size(32,32), Size(0,0),locations2);

   Mat Hogfeat;
   for(int i=0; i<features.size(); i++)<float>(i,0);

   Mat Hogfeat2;
   for(int i=0; i<features2.size(); i++)<float>(i,0);

   double distance=0;
   for(int i=0; i<Hogfeat.rows; i++)
      distance+= abs(<float>(i,0) -<float>(i,0));
   cout << distance <<endl;

   return 0;

R2.jpg R2.jpg R2-roi.jpg R2-roi.jpg