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Posted 20 Nov 2007

Kohonen's Self Organizing Maps in C++ with Application in Computer Vision Area

, 20 Nov 2007
The article demonstrates the self organizing maps clustering approach for unsupervised AI classification tasks with application examples in computer vision area for faces clustering and recognition
// stdafx.h : include file for standard system include files,
// or project specific include files that are used frequently, but
// are changed infrequently

#pragma once
#pragma warning(disable : 4996)

#include <iostream>
#include <tchar.h>

// TODO: reference additional headers your program requires here
#include <windows.h>
#include <stdio.h>
#include <math.h>
#include <wchar.h>
#include <conio.h>
#include <time.h>
#include <float.h>

#include <vector>
#include <algorithm>

using namespace std;

#include <mm3dnow.h>

typedef struct _entry {
        float *vec;                //vector
        int size;                  //vector size
        wchar_t fname[_MAX_PATH];  //vector file name
        int cls;                   //vector class

typedef struct _rec {
        vector<PENTRY> entries;         //rec entries
        vector< vector<int> > indices;  //2D array of classes indices
        vector<int> clsnum;             //classes numbers of indeces columns

   2D type array

    [entry ... vec ...]   (+ size,fname,class type)
    [entry ... vec ...]
    [entry ... vec ...]
    [entry ... vec ...]
    N = rec.size()

    individual point - rec.entries[y].vec[x]

    indices to y axis of entries vector
     vector<int> x0;  vector<int> x1; vector<int> x2; ... vector<int> xClassesNum

    clsnum[0],clsnum[1], ... clsnum[N]
      [x0][x1][x2][x3] ... [xN]    N different classes
      [x0][x1][x2][x3] ... [xN]
      [x0][x1][x2][x3] ... [xN]

      rec->clsnum[] =   3, 1, 2    <--  1D vector

                        0  1  8    <--  rec->indices[x].at(y)  2D array
                       10  5  9
                        3  2
                        4  6


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About the Author

Chesnokov Yuriy
Russian Federation Russian Federation
Highly skilled Engineer with 14 years of experience in academia, R&D and commercial product development supporting full software life-cycle from idea to implementation and further support. During my academic career I was able to succeed in MIT Computers in Cardiology 2006 international challenge, as a R&D and SW engineer gain CodeProject MVP, find algorithmic solutions to quickly resolve tough customer problems to pass product requirements in tight deadlines. My key areas of expertise involve Object-Oriented
Analysis and Design OOAD, OOP, machine learning, natural language processing, face recognition, computer vision and image processing, wavelet analysis, digital signal processing in cardiology.

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