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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
#pragma once

class CSignal

        int format;

        HANDLE fp, fpmap;
        LPVOID lpMap;

        bool read11(wchar_t *fname);           //obsolete
        //void read12(wchar_t *fname);         //obsolete
        bool read13(wchar_t *fname);

        void changeext(wchar_t *path, wchar_t *ext);

        CSignal(wchar_t *fname);                  //open existing file
        CSignal(wchar_t *fname, int n, int m);    //create new

        int N, M;                             //NxM size of mapped array
        vector<float *> data;                 //N array of pointers to filemapping
        wchar_t name[_MAX_PATH];              //file name

        void dump(wchar_t *fname);            //dump contents to text file

        void minmax(float *buff, int len, float &min, float &max);
        void nminmax(float *buff, int len, float a, float b);
        void nenergy(float *buff, int len, int L = 2);


    reads data from list file

	1.      file1  1
                file2  2
		file3  1

     files in separate files on disk    1.1 - simple text file
	                                1.2 - ecg like data (header in this file)
				        1.3 - mitbih like format (header in separate file *.hea  [N M])

    AI file format
    2.          file1  1
	        x1 x2 x3 ... xn
		file2  2
		x1 x2 x3 ... xn
		file3  1
		x1 x2 x3 ... xn

     files data in this list file


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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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