Leo_'s profile - activity

Great answer. Implemented this and getting great results.

Great answer. Implemented this and getting great results.

I am using code from bytefish.de 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
    lbp::ELBP(dst,lbp,1,8);
    lbp::ELBP(dst2,lbp2,1,8);

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

    //get histograms
    lbp::histogram(lbp,hist,255);
    lbp::histogram(lbp2,hist2,255);

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

    cout<<compareHist<<endl;

    waitKey(0);
    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?

Thanks.

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

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?

Thanks

Images: Thumbnails
R1
R1T
R2T

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.

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:

• Coin Detector

• Coin Recognition

@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?

@LorenaGdL What would you suggest instead of HOG?

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);
   cout<<features.size()<<endl;;

   hog2->compute(img2,features2,Size(32,32), Size(0,0),locations2);
   cout<<features2.size()<<endl;

   Mat Hogfeat;
   Hogfeat.create(features.size(),1,CV_32FC1);
   for(int i=0; i<features.size(); i++)
      Hogfeat.at<float>(i,0)=features.at(i);

   Mat Hogfeat2;
   Hogfeat2.create(features2.size(),1,CV_32FC1);
   for(int i=0; i<features2.size(); i++)
      Hogfeat2.at<float>(i,0)=features2.at(i);

   double distance=0;
   for(int i=0; i<Hogfeat.rows; i++)
   {
      distance+= abs(Hogfeat.at<float>(i,0) - Hogfeat2.at<float>(i,0));
   }
   cout << distance <<endl;

   return 0;
}

R2.jpg R2-roi.jpg