Sift matching C++ source code / using opencv library

Created Date : 2011.10

Language : C/C++

Tool : Microsoft Visual C++ 2008
Library & Utilized : OpenCV 2.3
Reference : SIFT reference
etc. : template Image, WebCam

I made SIFT matching program using OpenCV 2.3.
I was wondering how to know the object pose.
In the internet, there are many source about sift, surf. But most of code introduced about only descripter and matching. There is no code to find object pose.
So I made this code and I should disclose this code.


This code uses openCV functions very useful.
cvExtractSURF, cvFindHomography...

I made matching code to the class. Class file name is MareMatchingClass.h/cpp.
You can use my class in the source very easily.

1. Create Matching class
   CMareMatchingClass MMathing;   

2.Input PatchImg
   MMathing.ExtractPatchSurf(PatchImg);

3.Find PatchImg in the background img
   MMathing.GetObjectRectAndBestH(BackGroundImg, &rect4pt);

4.Drawing the rect(rect4pt).
5.Repeat, go to the 3.

The class is consist of like below process;
1. Extract Feature -> use cvExtractSURF function
2. Find Matching point
3. Select some feature in the mached feature points, randomly.
4. calculate Homography matrix. This is geometry relationship between patch and background image.
5. transform features in the patch image by Homography matrix.
6. compare the transformed features to the background features.
7. evaluate how much is the homography exact.
7. repeat 4~6 and select best H.

<source code>

I think the source code is not best.
There are still shortage the source code.
It would need futher improvemnet.
so I want to discuss with you. Please leave your valueable opinion.
Thank you.
Have a nice day~. ^^

Oh~ english is very difficult.....

STL vector(with 2 more elements) sorting code / STL에서 2개 이상의 엘리먼트를 가지고 있는 vector 정렬 code

The source is simple but I always don't remember the code well.
If you similar to me, refer to my code easily.

#include <iostream>
#include <cmath>
#include <time.h>
#include <vector>
#include <algorithm>
using namespace std;

class element{
public:
    element(float a, int b){
        value = a;
        index = b;
    }
    float value;
    int index;
};

bool compare(const element &a, const element &b )
{
    return a.value < b.value; //오름차순(ascending order)
    //return a.value > b.value; //내림차순(descending order)
}

void main()
{
    srand( (unsigned int)time(NULL));

    vector< element > A;

    // 값 넣기 (input value)/////////////////////////////////////////////
    for(int i=0; i<20000; ++i){
        A.push_back( element(rand()%10, i) );
    }


    //값 출력  (data print)///////////////////////////////////////////////
    printf("정렬 전(Before ordering) \n");
    for(i=0; i<20000; ++i){
        printf("A[%d] -> value %f :: index -> %d\n", i, A[i].value, A[i].index );
    }


    //정렬 (sorting)
    sort(A.begin(),A.end(),compare);


    //값 출력 (Value print)
    printf("정렬 후(After ordering)\n");
    for(i=0; i<20000; ++i){
        printf("A[%d] -> value %f :: index -> %d\n", i, A[i].value, A[i].index );
    }
      
}

Incremental K-means matlab source code

Created Date : 2011.10.

Language : Matlab 2010

Tool : -

Library & Utilized :-

Reference :An Incremental K-means algorithm(D.T. Pham, S.S. Dimov and C.D. Nguyen)

Etc. :-

I made Incremental K-means algorithm as matlab source code.
I made the code base on above table which is introduced in the paper.
The incremental K-means is similar to K-means but the different point is number of cluster class is increasing. but we have to set the maximum number.

Below figure is result of clustering. the sample data is normal vector of the 3D point that is acquied by bumblebee.
I think the K-means algorithm is sensitive to error or outlier data. because the algorithm use euclidean distance. and The result of clustering is different every time because the initial position of the class is selected randomly.

I hope my code help to your problem and study.
thank you.
Give me your valuable comments. ^^

<source code>

 

Clustering result of the sample datas.

enlarge image of the above figure