diff --git a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp
index 34cc5885a9..fd0d9f46ee 100644
--- a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp
+++ b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp
@@ -426,6 +426,8 @@ protected:
     Ptr<CvHaarClassifierCascade> oldCascade;
 };
 
+void CV_EXPORTS_W groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
+
 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
 
 struct CV_EXPORTS_W HOGDescriptor
@@ -574,6 +576,110 @@ protected:
     FernClassifier fernClassifier;
 };
 
+/****************************************************************************************\
+*                           Dominant Orientation Templates                               *
+\****************************************************************************************/
+
+class CV_EXPORTS DOTDetector
+{
+public:
+    struct TrainParams
+    {
+        enum { BIN_COUNT = 7 };
+        static double BIN_RANGE() { return 180.0 / BIN_COUNT; }
+
+        TrainParams();
+        TrainParams( const Size& winSize, int regionSize=7, int minMagnitude=60,
+                     int maxStrongestCount=7, int maxNonzeroBits=6,
+                     float minRatio=0.85f );
+
+        void read( FileNode& fn );
+        void write( FileStorage& fs ) const;
+
+        void asserts() const;
+
+        Size winSize;
+        int regionSize;
+
+        int minMagnitude;
+        int maxStrongestCount;
+        int maxNonzeroBits;
+
+        float minRatio;
+    };
+
+    struct DetectParams
+    {
+        DetectParams();
+        DetectParams( float minRatio, int minRegionSize, int maxRegionSize, int regionSizeStep,
+                      bool isGroup, int groupThreshold, double groupEps );
+
+        void asserts( float minTrainRatio=1.f) const;
+
+        float minRatio;
+
+        int minRegionSize;
+        int maxRegionSize;
+        int regionSizeStep;
+
+        bool isGroup;
+
+        int groupThreshold;
+        double groupEps;
+    };
+
+    struct DOTTemplate
+    {
+        DOTTemplate();
+        DOTTemplate( const cv::Mat& quantizedImage, int classID,
+                     const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& gradientMask=cv::Mat() );
+        void addClassID( int classID, const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& gradientMask=cv::Mat() );
+
+        static float computeTexturelessRatio( const cv::Mat& quantizedImage );
+
+        void read( FileNode& fn );
+        void write( FileStorage& fs ) const;
+
+        cv::Mat quantizedImage;
+        std::vector<int> classIDs;
+        float texturelessRatio;
+
+        std::vector<cv::Mat> maskedImages;
+        std::vector<cv::Mat> gradientMasks;
+    };
+
+    DOTDetector();
+    DOTDetector( const std::string& filename ); // load from xml-file
+
+    virtual ~DOTDetector();
+    void clear();
+
+    void read( FileNode& fn );
+    void write( FileStorage& fs ) const;
+
+    void load( const std::string& filename );
+    void save( const std::string& filename ) const;
+
+    void train( const string& baseDirName, const TrainParams& trainParams=TrainParams(), bool isAddImageAndGradientMask=false );
+    void detectMultiScale( const Mat& image, vector<vector<Rect> >& rects,
+                           const DetectParams& detectParams=DetectParams(),
+                           vector<vector<float> >*ratios=0, vector<vector<int> >* trainTemplateIndices=0 ) const;
+
+    const vector<DOTTemplate>& getDOTTemplates() const;
+    const vector<string>& getClassNames() const;
+
+    static void groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps );
+
+protected:
+    void detectQuantized( const Mat& queryQuantizedImage, float minRatio,
+                          vector<vector<Rect> >& rects, vector<vector<float> >& ratios, vector<vector<int> >& trainTemlateIdxs ) const;
+    TrainParams trainParams;
+    bool isAddImageAndGradientMask;
+
+    std::vector<std::string> classNames;
+    std::vector<DOTTemplate> dotTemplates;
+};
+
 }
 
 /****************************************************************************************\
diff --git a/modules/objdetect/src/dotdetector.cpp b/modules/objdetect/src/dotdetector.cpp
new file mode 100644
index 0000000000..5690838867
--- /dev/null
+++ b/modules/objdetect/src/dotdetector.cpp
@@ -0,0 +1,855 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+#include <iostream>
+#include <fstream>
+
+using namespace std;
+
+namespace cv
+{
+
+/*
+ * Functions
+ */
+
+static void readDirContent( const string& descrFilename, vector<string>& names )
+{
+    names.clear();
+
+    ifstream file( descrFilename.c_str() );
+    if ( !file.is_open() )
+        return;
+
+    while( !file.eof() )
+    {
+        string str; getline( file, str );
+        if( str.empty() ) break;
+        if( str[0] == '#' ) continue; // comment
+        names.push_back(str);
+    }
+    file.close();
+}
+
+static void computeGradients( const Mat& image, Mat& magnitudes, Mat& angles )
+{
+    Mat dx, dy;
+    cv::Sobel( image, dx, CV_32F, 1, 0, 3 );
+    cv::Sobel( image, dy, CV_32F, 0, 1, 3 );
+
+    cv::cartToPolar( dx, dy, magnitudes, angles, true );
+    CV_Assert( magnitudes.type() == CV_32FC1 );
+    CV_Assert( angles.type() == CV_32FC1 );
+}
+
+static void computeWinData( const Mat& image, const Mat& mask, const Size& winSize,
+                            Mat& winImage, Mat& winMask,
+                            Mat& winMagnitudes, Mat& winAngles, int border=0 )
+{
+    CV_Assert( border >= 0 );
+
+    Size extSize;
+    extSize.width = winSize.width + 2*border;
+    extSize.height = winSize.height + 2*border;
+
+    if( mask.empty() )
+    {
+        image.copyTo( winImage );
+        winMask.release();
+    }
+    else
+    {
+        vector<Point> points;
+        points.reserve( image.rows * image.cols );
+        for( int y = 0; y < mask.rows; y++ )
+        {
+            for( int x = 0; x < mask.cols; x++ )
+            {
+                if( mask.at<uchar>(y,x) )
+                    points.push_back( cv::Point(x,y) );
+            }
+        }
+
+        cv::Rect_<float> brect = cv::boundingRect( cv::Mat(points) );
+
+        float ratio = std::min( (float)winSize.width/ brect.width, (float)winSize.height / brect.height );
+
+        float rectWidth = winSize.width / ratio;
+        float rectHeight = winSize.height / ratio;
+
+        float scaledBorder = border / ratio;
+        brect.x -= (rectWidth - brect.width) / 2.f + scaledBorder ;
+        brect.y -= (rectHeight - brect.height) / 2.f + scaledBorder;
+        brect.width = rectWidth + 2*scaledBorder;
+        brect.height = rectHeight + 2*scaledBorder;
+
+        // TODO the following cases:
+        assert( Rect(0, 0, image.cols, image.rows ).contains( brect.tl() ) );
+        assert( Rect(0, 0, image.cols, image.rows ).contains( brect.br() ) );
+
+        Mat subImage( image, brect );
+        Mat subMask( mask, brect );
+
+        cv::resize( subImage, winImage, extSize );
+        cv::resize( subMask, winMask, extSize );
+
+        CV_Assert( winImage.size() == extSize );
+        CV_Assert( winMask.size() == extSize );
+    }
+
+    computeGradients( winImage, winMagnitudes, winAngles );
+
+    //    Mat bluredWinImage;
+    //    cv::GaussianBlur( winImage, bluredWinImage, Size(), 0.5, 0.5 );
+    //    computeGradients( bluredWinImage, magnitudes, angles );
+}
+
+inline int getBin( double angle )
+{
+    double angle1 = angle >= 180 ? angle - 180 : angle;
+    int orientationBin = (int)(angle1 / DOTDetector::TrainParams::BIN_RANGE() );
+    assert( orientationBin < 7 );
+
+    return orientationBin;
+}
+
+static void copyTrainData( const Mat& magnitudesSrc, const Mat& anglesSrc, const Mat& mask,
+                           Mat& magnitudesDst, Mat& anglesDst )
+{
+    magnitudesDst = Mat( magnitudesSrc.size(), magnitudesSrc.type(), Scalar::all(-1) );
+    anglesDst = Mat( anglesSrc.size(), anglesSrc.type(), Scalar::all(-1) );
+
+    magnitudesSrc.copyTo( magnitudesDst, mask );
+    anglesSrc.copyTo( anglesDst, mask );
+}
+
+inline int countNonZeroBits( uchar val )
+{
+    uchar v = val;
+    v = (v & 0x55) + ((v >> 1) & 0x55);
+    v = (v & 0x33) + ((v >> 2) & 0x33);
+
+    return (v & 0x0f) + ((v >> 4) & 0x0f);
+}
+
+inline void countNonZeroAndTexturelessBits( const Mat& mat, int& nonZeroBitsCount, int& texturelessBitsCount )
+{
+    CV_Assert( mat.type() == CV_8UC1 );
+
+    nonZeroBitsCount = 0;
+    texturelessBitsCount = 0;
+
+    const uchar texturelessValue = 1 << DOTDetector::TrainParams::BIN_COUNT;
+    for( int y = 0; y < mat.rows; y++ )
+    {
+        for( int x = 0; x < mat.cols; x++ )
+        {
+            int curCount = countNonZeroBits( mat.at<uchar>(y,x) );
+            if( curCount )
+            {
+                nonZeroBitsCount += curCount;
+                if( mat.at<uchar>(y,x) == texturelessValue )
+                    texturelessBitsCount++;
+            }
+        }
+    }
+}
+
+static void quantizeToTrain( const Mat& _magnitudesExt, const Mat& _anglesExt, const Mat& maskExt,
+                             Mat& quantizedImage, const DOTDetector::TrainParams& params )
+{
+    CV_DbgAssert( params.winSize.height % params.regionSize == 0 );
+    CV_DbgAssert( params.winSize.width % params.regionSize == 0 );
+    CV_DbgAssert( params.regionSize % 2 == 1 );
+
+    const int regionSize_2 = params.regionSize / 2;
+
+    Mat magnitudesExt, anglesExt;
+    copyTrainData( _magnitudesExt, _anglesExt, maskExt, magnitudesExt, anglesExt );
+
+    const int verticalRegionCount = params.winSize.height / params.regionSize;
+    const int horizontalRegionCount = params.winSize.width / params.regionSize;
+
+    quantizedImage = Mat( verticalRegionCount, horizontalRegionCount, CV_8UC1, Scalar::all(0) );
+
+    Rect curRect( regionSize_2, regionSize_2, params.regionSize, params.regionSize );
+
+    for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
+    {
+        for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
+        {
+            uchar curRectBits = 0;
+
+            for( int yShift = -regionSize_2; yShift <= regionSize_2; yShift++ ) // TODO yShift += regionSize/2
+            {
+                Rect shiftedRect = curRect;
+
+                shiftedRect.y = curRect.y + yShift;
+
+                for( int xShift = -regionSize_2; xShift <= regionSize_2; xShift++ ) // TODO xShift += regionSize/2
+                {
+                    shiftedRect.x = curRect.x + xShift;
+
+                    Mat subMagnitudes( magnitudesExt, shiftedRect ), subMagnitudesCopy;
+                    subMagnitudes.copyTo( subMagnitudesCopy );
+                    Mat subAngles( anglesExt, shiftedRect );
+
+                    double maxMagnitude;
+                    int strongestCount = 0;
+                    for( ; strongestCount < params.maxStrongestCount; strongestCount++ )
+                    {
+                        Point maxLoc;
+                        cv::minMaxLoc( subMagnitudesCopy, 0, &maxMagnitude, 0, &maxLoc );
+
+                        if( maxMagnitude < params.minMagnitude )
+                            break;
+
+                        subMagnitudesCopy.at<float>( maxLoc ) = -1;
+
+                        double angle = subAngles.at<float>( maxLoc );
+                        int orientationBin = getBin( angle );
+
+                        curRectBits |= 1 << orientationBin;
+                    }
+                    if( strongestCount == 0 && maxMagnitude > 0 )
+                        curRectBits |= 1 << DOTDetector::TrainParams::BIN_COUNT;
+                }
+            }
+
+            if( !( curRectBits == (1 << DOTDetector::TrainParams::BIN_COUNT) && cv::countNonZero(magnitudesExt(curRect) == -1) ) )
+            {
+                if( countNonZeroBits( curRectBits ) <= params.maxNonzeroBits )
+                    quantizedImage.at<uchar>(vRegIdx, hRegIdx) = curRectBits;
+            }
+
+            curRect.x += params.regionSize;
+        }
+        curRect.x = regionSize_2;
+        curRect.y += params.regionSize;
+    }
+}
+
+static void quantizeToDetect( const Mat& _magnitudes, const Mat& angles, Mat& quantizedImage, const DOTDetector::TrainParams& params )
+{
+    Mat magnitudes; _magnitudes.copyTo( magnitudes );
+
+    const int verticalRegionCount = magnitudes.rows / params.regionSize;
+    const int horizontalRegionCount = magnitudes.cols / params.regionSize;
+
+    quantizedImage = Mat( verticalRegionCount, horizontalRegionCount, CV_8UC1, Scalar::all(0) );
+
+    Rect curRect(0, 0, params.regionSize, params.regionSize);
+    const int maxStrongestCount = 1;
+    for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
+    {
+        for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
+        {
+            uchar curRectBits = 0;
+
+            Mat subMagnitudes( magnitudes, curRect ), subMagnitudesCopy;
+            subMagnitudes.copyTo( subMagnitudesCopy );
+            Mat subAngles( angles, curRect );
+
+            double maxMagnitude = -1;
+            int strongestCount = 0;
+
+            for( ; strongestCount < maxStrongestCount; strongestCount++ )
+            {
+                Point maxLoc;
+                cv::minMaxLoc( subMagnitudesCopy, 0, &maxMagnitude, 0, &maxLoc );
+
+                if( maxMagnitude < params.minMagnitude )
+                    break;
+
+                subMagnitudesCopy.at<float>( maxLoc ) = -1;
+
+                double angle = subAngles.at<float>( maxLoc );
+                int orientationBin = getBin( angle );
+
+                curRectBits |= 1 << orientationBin;
+            }
+            if( strongestCount == 0 && maxMagnitude > 0 )
+                curRectBits |= 1 << DOTDetector::TrainParams::BIN_COUNT;
+
+            quantizedImage.at<uchar>(vRegIdx, hRegIdx) = curRectBits;
+            curRect.x += params.regionSize;
+        }
+        curRect.x = 0;
+        curRect.y += params.regionSize;
+    }
+}
+
+inline void andQuantizedImages( const Mat& queryQuantizedImage, const Mat& trainQuantizedImage, float& ratio, float& texturelessRatio )
+{
+    int nonZeroCount = 0, texturelessCount = 0;
+    countNonZeroAndTexturelessBits( trainQuantizedImage & queryQuantizedImage, nonZeroCount, texturelessCount );
+
+    CV_Assert( nonZeroCount > 0 );
+    int area = cv::countNonZero( trainQuantizedImage );
+
+    ratio = (float)nonZeroCount / area;
+    texturelessRatio = texturelessCount / nonZeroCount;
+}
+
+static void computeTrainUsedStrongestMask( const Mat& _magnitudesExt, const Mat& _anglesExt, const Mat& maskExt, const Mat& quantizedImage,
+                                           Mat& winUsedStrongestMask, int regionSize, int minMagnitude )
+{
+    const int usedLabel = 255;
+    const int regionSize_2 = regionSize / 2;
+
+    Mat magnitudesExt, anglesExt;
+    copyTrainData( _magnitudesExt, _anglesExt, maskExt, magnitudesExt, anglesExt );
+
+    const int verticalRegionCount = quantizedImage.rows;
+    const int horizontalRegionCount = quantizedImage.cols;
+
+    Mat binsExt( anglesExt.size(), CV_32SC1, Scalar::all(-1) );
+    for( int y = 0; y < binsExt.rows; y++ )
+    {
+        for( int x = 0; x < binsExt.cols; x++ )
+        {
+            if( magnitudesExt.at<float>(y,x) >= minMagnitude )
+            {
+                binsExt.at<int>(y,x) = getBin( anglesExt.at<float>(y,x) );
+            }
+        }
+    }
+
+    Rect curRect( 0, 0, regionSize + 2*regionSize_2, regionSize + 2*regionSize_2 );
+
+    Mat colorsExt( anglesExt.size(), CV_8UC1, Scalar::all(0) );
+    for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
+    {
+        for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
+        {
+            Mat subColors = colorsExt( curRect );
+            Mat subBins = binsExt( curRect );
+
+            uchar bits = quantizedImage.at<uchar>(vRegIdx, hRegIdx);
+
+            for( int binIdx = 0; binIdx < DOTDetector::TrainParams::BIN_COUNT; binIdx++ )
+            {
+                if( bits & (1 << binIdx) )
+                {
+                    float gray = usedLabel/* * weights[vRegIdx*horizontalRegionCount+hRegIdx][binIdx]*/;
+                    subColors.setTo( Scalar((uchar)cvRound(gray)), subBins == binIdx );
+                }
+            }
+
+            curRect.x += regionSize;
+        }
+
+        curRect.x = 0;
+        curRect.y += regionSize;
+    }
+
+    Mat colors = colorsExt( Rect(regionSize_2, regionSize_2, binsExt.cols - 2*regionSize_2, binsExt.rows - 2*regionSize_2) );
+
+    colors.convertTo( winUsedStrongestMask, CV_8UC1 );
+}
+
+/*
+ * DOTDetector::Params
+ */
+
+DOTDetector::TrainParams::TrainParams() : winSize(Size(84,84)), regionSize(7),
+                                          minMagnitude(60), maxStrongestCount(7), maxNonzeroBits(6),
+                                          minRatio(0.85f) {}
+
+DOTDetector::TrainParams::TrainParams( const Size& _winSize, int _regionSize, int _minMagnitude,
+                                       int _maxStrongestCount, int _maxNonzeroBits,
+                                       float _minRatio ) :
+                                winSize(_winSize), regionSize(_regionSize), minMagnitude(_minMagnitude),
+                                maxStrongestCount(_maxStrongestCount), maxNonzeroBits(_maxNonzeroBits),
+                                minRatio(_minRatio)
+{
+    asserts();
+}
+
+void DOTDetector::TrainParams::asserts() const
+{
+    CV_Assert( winSize.width > 0 && winSize.height > 0 );
+    CV_Assert( regionSize > 0 && regionSize % 2 == 1);
+
+    CV_Assert( winSize.width % regionSize == 0 );
+    CV_Assert( winSize.height % regionSize == 0 );
+
+    CV_Assert( minMagnitude > 0 );
+
+    CV_Assert( maxStrongestCount > 0 && maxStrongestCount <= BIN_COUNT );
+    CV_Assert( maxNonzeroBits > 0 && maxNonzeroBits <= BIN_COUNT );
+
+    CV_Assert( minRatio > 0.f && minRatio < 1.f );
+}
+
+void DOTDetector::TrainParams::read( FileNode& fn )
+{
+    winSize.width = fn["winSize.width"];
+    winSize.height = fn["winSize.height"];
+    regionSize = fn["regionSize"];
+
+    minMagnitude = fn["minMagnitude"];
+    maxStrongestCount = fn["maxStrongestCount"];
+    maxNonzeroBits = fn["maxNonzeroBits"];
+
+    minRatio = fn["minRatio"];
+
+    asserts();
+}
+
+void DOTDetector::TrainParams::write( FileStorage& fs ) const
+{
+    CV_Assert( fs.isOpened() );
+
+    fs << "winSize.width" << winSize.width;
+    fs << "winSize.height" << winSize.height;
+    fs << "regionSize" << regionSize;
+
+    fs << "minMagnitude" << minMagnitude;
+    fs << "maxStrongestCount" << maxStrongestCount;
+    fs << "maxNonzeroBits" << maxNonzeroBits;
+
+    fs << "minRatio" << minRatio;
+}
+
+DOTDetector::DetectParams::DetectParams() : minRatio(0.8f), minRegionSize(7), maxRegionSize(9), regionSizeStep(2),
+                                            isGroup(true), groupThreshold(3), groupEps(0.2) {}
+
+DOTDetector::DetectParams::DetectParams( float _minRatio, int _minRegionSize, int _maxRegionSize, int _regionSizeStep,
+                                         bool _isGroup, int _groupThreshold, double _groupEps ) :
+    minRatio(_minRatio), minRegionSize(_minRegionSize), maxRegionSize(_maxRegionSize), regionSizeStep(_regionSizeStep),
+    isGroup(_isGroup), groupThreshold(_groupThreshold), groupEps(_groupEps)
+{
+    asserts();
+}
+
+void DOTDetector::DetectParams::asserts( float minTrainRatio ) const
+{
+    CV_Assert( minRatio > 0 && minRatio < 1 );
+    CV_Assert( minRatio <= minTrainRatio );
+
+    CV_Assert( minRegionSize > 0 && minRegionSize % 2 == 1 );
+    CV_Assert( maxRegionSize > 0 && maxRegionSize % 2 == 1 );
+    CV_Assert( minRegionSize <= maxRegionSize );
+
+    CV_Assert( regionSizeStep % 2 == 0 );
+
+    if( isGroup )
+    {
+        CV_Assert( groupThreshold > 0 );
+        CV_Assert( groupEps > 0 && groupEps < 1 );
+    }
+}
+
+/*
+ * DOTDetector::DOTTemplate
+ */
+
+DOTDetector::DOTTemplate::DOTTemplate() : texturelessRatio(-1.f) {}
+
+DOTDetector::DOTTemplate::DOTTemplate( const cv::Mat& _quantizedImage, int _classID, const cv::Mat& _maskedImage, const cv::Mat& _gradientMask ) :
+        quantizedImage(_quantizedImage), texturelessRatio(computeTexturelessRatio(_quantizedImage))
+{
+    addClassID( _classID, _maskedImage, _gradientMask );
+}
+
+void DOTDetector::DOTTemplate::addClassID( int _classID, const cv::Mat& _maskedImage, const cv::Mat& _gradientMask )
+{
+    CV_Assert( _classID >= 0 );
+    bool isFound = false;
+
+    for( size_t i = 0; i < classIDs.size(); i++ )
+    {
+        if( classIDs[i] == _classID )
+        {
+            isFound = true;
+            break;
+        }
+    }
+
+    if( !isFound )
+    {
+        classIDs.push_back( _classID );
+        if( !_maskedImage.empty() )
+        {
+            CV_Assert( !_gradientMask.empty() );
+            maskedImages.push_back( _maskedImage );
+            gradientMasks.push_back( _gradientMask );
+        }
+    }
+}
+
+float DOTDetector::DOTTemplate::computeTexturelessRatio( const cv::Mat& quantizedImage )
+{
+    const uchar TEXTURELESS_VAL = 1 << DOTDetector::TrainParams::BIN_COUNT;
+    int texturelessCount = 0;
+    for( int y = 0; y < quantizedImage.rows; y++ )
+    {
+        for( int x = 0; x < quantizedImage.cols; x++ )
+        {
+            if( quantizedImage.at<uchar>(y,x) & TEXTURELESS_VAL )
+                texturelessCount++;
+        }
+    }
+    return (float)texturelessCount/ (float)(quantizedImage.cols * quantizedImage.rows);
+}
+
+void DOTDetector::DOTTemplate::read( FileNode& fn )
+{
+    fn["template"] >> quantizedImage;
+    fn["classIDs"] >> classIDs;
+    texturelessRatio = fn["texturelessRatio"];
+}
+
+void DOTDetector::DOTTemplate::write( FileStorage& fs ) const
+{
+    fs << "template" << quantizedImage;
+    fs << "classIDs" << classIDs;
+    fs << "texturelessRatio" << texturelessRatio;
+}
+
+/*
+ * DOTDetector
+ */
+
+DOTDetector::DOTDetector() : isAddImageAndGradientMask( false )
+{
+}
+
+DOTDetector::DOTDetector( const std::string& filename ) : isAddImageAndGradientMask( false )
+{
+    load( filename );
+}
+
+DOTDetector::~DOTDetector()
+{
+    clear();
+}
+
+void DOTDetector::clear()
+{
+    classNames.clear();
+    dotTemplates.clear();
+}
+
+void DOTDetector::read( FileNode& fn )
+{
+    clear();
+
+    // read params
+    FileNode fn_params = fn["train_params"];
+    trainParams.read( fn_params );
+
+    // read class names
+    int classCount = fn["class_count"];
+    FileNodeIterator fni = fn["class_names"].begin();
+    for( int i = 0; i < classCount; i++ )
+    {
+        string name;
+        fni >> name;
+        classNames.push_back( name );
+    }
+
+    // read DOT templates
+    int templatesCount = fn["templates_count"];
+    fni = fn["templates"].begin();
+    for( int i = 0; i < templatesCount; i++ )
+    {
+        dotTemplates.push_back( DOTTemplate() );
+        FileNode cur_fn = *fni;
+        dotTemplates.rbegin()->read( cur_fn );
+    }
+}
+
+void DOTDetector::write( FileStorage& fs ) const
+{
+    // write params
+    fs << "train_params" << "{";
+    trainParams.write( fs );
+    fs << "}"; //params
+
+    // write class names
+    fs << "class_count" << (int)classNames.size();
+    fs << "class_names" << "[";
+    for( size_t i = 0; i < classNames.size(); i++ )
+    {
+        fs << classNames[i];
+    }
+    fs << "]";
+
+    // write dot templates
+    fs << "templates_count" << (int)dotTemplates.size();
+    fs << "templates" << "[";
+    for( size_t i = 0; i < dotTemplates.size(); i++ )
+    {
+        dotTemplates[i].write( fs );
+    }
+    fs << "]";
+}
+
+void DOTDetector::load( const std::string& filename )
+{
+    FileStorage fs( filename, FileStorage::READ );
+    if( fs.isOpened() )
+    {
+        FileNode fn = fs.getFirstTopLevelNode();
+        read( fn );
+    }
+}
+
+void DOTDetector::save( const std::string& filename ) const
+{
+    FileStorage fs( filename, FileStorage::WRITE );
+    if( fs.isOpened() )
+    {
+        fs << "dot_detector" << "{";
+        write( fs );
+        fs << "}";
+    }
+}
+
+void DOTDetector::train( const string& _baseDirName, const TrainParams& _trainParams, bool _isAddImageAndGradientMask )
+{
+    clear();
+
+    trainParams = _trainParams;
+    trainParams.asserts();
+
+    string baseDirName = _baseDirName + (*(_baseDirName.end()-1) == '/' ? "" : "/");
+    const int regionSize_2 = trainParams.regionSize / 2;
+
+    readDirContent( baseDirName+"objects.txt", classNames );
+
+    for( size_t objIdx = 0; objIdx < classNames.size(); objIdx++ )
+    {
+        string curObjDirName = baseDirName + classNames[objIdx] + "/";
+
+        cout << "===============" << classNames[objIdx] << "===============" << endl;
+        vector<string> imagesFilenames;
+        readDirContent( curObjDirName + "images.txt", imagesFilenames );
+
+        if( imagesFilenames.empty() )
+            continue;
+
+        int countSamples = 0;
+        for( size_t imgIdx = 0; imgIdx < imagesFilenames.size(); imgIdx++ )
+        {
+            cout << imagesFilenames[imgIdx] ;
+            Mat image = cv::imread( curObjDirName + imagesFilenames[imgIdx], 0 );
+            if( image.empty() )
+                continue;
+
+            Mat mask;
+            {
+                Mat _mask = cv::imread( curObjDirName + imagesFilenames[imgIdx] + ".mask.png", 0 );
+                if( _mask.empty() )
+                {
+                    cout << " - FAIL" << endl;
+                    continue;
+                }
+                mask = _mask;
+            }
+            cout << " - OK" << endl;
+
+            countSamples++;
+
+            Mat trainImageExt, trainMaskExt, trainQuantizedImage, detectQuantizedImage;
+            Mat trainMagnitudesExt, trainAnglesExt;
+
+            computeWinData( image, mask, trainParams.winSize,
+                            trainImageExt, trainMaskExt,
+                            trainMagnitudesExt, trainAnglesExt, regionSize_2 );
+
+            quantizeToTrain( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage, trainParams );
+
+            quantizeToDetect( trainMagnitudesExt, trainAnglesExt, detectQuantizedImage, trainParams );
+
+            vector<vector<Rect> > rects;
+            vector<vector<float> > ratios;
+            vector<vector<int> > trainTemplatesIdxs;
+            detectQuantized( detectQuantizedImage, trainParams.minRatio, rects, ratios, trainTemplatesIdxs );
+
+            Mat maskedTrainImage, trainGradientMask;
+            if( isAddImageAndGradientMask )
+            {
+                trainImageExt.copyTo( maskedTrainImage, trainMaskExt);
+                computeTrainUsedStrongestMask( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage,
+                                               trainGradientMask, trainParams.regionSize, trainParams.minMagnitude );
+            }
+            int classID = classNames.size()-1;
+            bool isFound = false;
+            for( size_t cIdx = 0; cIdx < trainTemplatesIdxs.size(); cIdx++ )
+            {
+                if( trainTemplatesIdxs[cIdx].size() )
+                {
+                    for( size_t i = 0; i < trainTemplatesIdxs[cIdx].size(); i++ )
+                    {
+                        int tIdx = trainTemplatesIdxs[cIdx][i];
+
+                        dotTemplates[tIdx].addClassID( classID, maskedTrainImage, trainGradientMask );
+                        isFound = true;
+                    }
+                }
+            }
+            if( !isFound )
+            {
+                dotTemplates.push_back( DOTTemplate(trainQuantizedImage, classID, maskedTrainImage, trainGradientMask) );
+            }
+
+            cout << "dot templates size = " << dotTemplates.size() << endl;
+        }
+    }
+}
+
+void DOTDetector::detectQuantized( const Mat& testQuantizedImage, float minRatio,
+                                   vector<vector<Rect> >& rects, vector<vector<float> >& ratios, vector<vector<int> >& trainTemlateIdxs ) const
+{
+    if( dotTemplates.empty() )
+        return;
+
+    const int regionsPerRow = dotTemplates[0].quantizedImage.rows;
+    const int regionsPerCol = dotTemplates[0].quantizedImage.cols;
+
+    int classCount = classNames.size();
+
+    rects.resize( classCount );
+    ratios.resize( classCount );
+    trainTemlateIdxs.resize( classCount );
+
+    for( size_t tIdx = 0; tIdx < dotTemplates.size(); tIdx++ )
+    {
+        Rect r( 0, 0, regionsPerCol, regionsPerRow );
+        for( r.y = 0; r.y <= testQuantizedImage.rows-r.height; r.y++ )
+        {
+            for( r.x = 0; r.x <= testQuantizedImage.cols-r.width; r.x++ )
+            {
+                float ratio, texturelessRatio;
+                andQuantizedImages( testQuantizedImage(r), dotTemplates[tIdx].quantizedImage, ratio, texturelessRatio );
+                if( ratio > minRatio && texturelessRatio < dotTemplates[tIdx].texturelessRatio )
+                {
+                    for( size_t cIdx = 0; cIdx < dotTemplates[tIdx].classIDs.size(); cIdx++ )
+                    {
+                        int classID =  dotTemplates[tIdx].classIDs[cIdx];
+                        rects[classID].push_back( r );
+                        ratios[classID].push_back( ratio );
+                        trainTemlateIdxs[classID].push_back( tIdx );
+                    }
+                }
+            }
+        }
+    }
+}
+
+void DOTDetector::detectMultiScale( const Mat& image, vector<vector<Rect> >& rects,
+                                    const DetectParams& detectParams, vector<vector<float> >* ratios, vector<vector<int> >* trainTemplateIndices ) const
+{
+    detectParams.asserts( trainParams.minRatio );
+
+    int classCount = classNames.size();
+    rects.resize( classCount );
+    if( ratios )
+    {
+        ratios->clear();
+        if( !detectParams.isGroup )
+            ratios->resize( classCount );
+    }
+    if( trainTemplateIndices )
+    {
+        trainTemplateIndices->clear();
+        if( !detectParams.isGroup )
+            trainTemplateIndices->resize( classCount );
+    }
+
+    Mat magnitudes, angles;
+    computeGradients( image, magnitudes, angles );
+    for( int regionSize = detectParams.minRegionSize; regionSize <= detectParams.maxRegionSize; regionSize += detectParams.regionSizeStep )
+    {
+        Mat quantizedImage;
+        vector<vector<Rect> > curRects;
+        vector<vector<float> > curRatios;
+        vector<vector<int> > curTrainTemlateIdxs;
+        quantizeToDetect( magnitudes, angles, quantizedImage, trainParams );
+        detectQuantized( quantizedImage, detectParams.minRatio, curRects, curRatios, curTrainTemlateIdxs );
+
+        for( int ci = 0; ci < classCount; ci++ )
+        {
+            for( size_t ri = 0; ri < curRects[ci].size(); ri++  )
+            {
+                Rect r = curRects[ci][ri];
+                r.x *= regionSize;
+                r.y *= regionSize;
+                r.width *= regionSize;
+                r.height *= regionSize;
+
+                rects[ci].push_back( r );
+                if( ratios && !detectParams.isGroup )
+                    (*ratios)[ci].push_back( curRatios[ci][ri] );
+                if( trainTemplateIndices && !detectParams.isGroup )
+                    (*trainTemplateIndices)[ci].push_back( curTrainTemlateIdxs[ci][ri] );
+            }
+        }
+    }
+
+    if( detectParams.isGroup )
+        groupRectanglesList( rects, detectParams.groupThreshold, detectParams.groupEps );
+}
+
+
+const vector<DOTDetector::DOTTemplate>& DOTDetector::getDOTTemplates() const
+{
+    return dotTemplates;
+}
+
+const vector<string>& DOTDetector::getClassNames() const
+{
+    return classNames;
+}
+
+void DOTDetector::groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps )
+{
+    for( size_t i = 0; i < rectList.size(); i++ )
+        groupRectangles( rectList[i], groupThreshold, eps );
+}
+
+} // namespace cv
+
+/* End of file. */
diff --git a/samples/cpp/dot.cpp b/samples/cpp/dot.cpp
new file mode 100644
index 0000000000..dfcedad711
--- /dev/null
+++ b/samples/cpp/dot.cpp
@@ -0,0 +1,134 @@
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/objdetect/objdetect.hpp>
+#include <iostream>
+#include <fstream>
+
+using namespace cv;
+using namespace std;
+
+#define SHOW_ALL_RECTS_BY_ONE 1
+static void fillColors( vector<Scalar>& colors )
+{
+    cv::RNG rng = theRNG();
+
+    for( size_t ci = 0; ci < colors.size(); ci++ )
+        colors[ci] = Scalar( rng(256), rng(256), rng(256) );
+}
+
+static void readTestImageNames( const string& descrFilename, vector<string>& names )
+{
+    names.clear();
+
+    ifstream file( descrFilename.c_str() );
+    if ( !file.is_open() )
+        return;
+
+    while( !file.eof() )
+    {
+        string str; getline( file, str );
+        if( str.empty() ) break;
+        if( str[0] == '#' ) continue; // comment
+        names.push_back(str);
+    }
+    file.close();
+}
+
+int main( int argc, char **argv )
+{
+    if( argc != 1 && argc != 3 )
+    {
+        cout << "Format: base; " << endl << "or without arguments to use default data" << endl;
+        return -1;
+    }
+
+    string baseDirName, testDirName;
+    if( argc == 1 )
+    {
+        baseDirName = "../MultiScaleDOT/Data/train/";
+        testDirName = "../MultiScaleDOT/Data/test/";
+    }
+    else
+    {
+        baseDirName = argv[1];
+        testDirName = argv[2];
+        baseDirName += (*(baseDirName.end()-1) == '/' ? "" : "/");
+        testDirName += (*(testDirName.end()-1) == '/' ? "" : "/");
+    }
+
+    DOTDetector::TrainParams trainParams;
+    trainParams.winSize = Size(84, 84);
+    trainParams.regionSize = 7;
+    trainParams.minMagnitude = 60; // we ignore pixels with magnitude less then minMagnitude
+    trainParams.maxStrongestCount = 7; // we find such count of strongest gradients for each region
+    trainParams.maxNonzeroBits = 6; // we filter very textured regions (that have more then maxUnzeroBits count of 1s (ones) in the template)
+    trainParams.minRatio = 0.85f;
+
+    // 1. Train detector
+    DOTDetector dotDetector;
+    dotDetector.train( baseDirName, trainParams, true );
+
+    const vector<string>& classNames = dotDetector.getClassNames();
+    const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates();
+
+    vector<Scalar> colors( classNames.size() );
+    fillColors( colors );
+    cout << "Templates count " << dotTemplates.size() << endl;
+
+    vector<string> testFilenames;
+    readTestImageNames( testDirName + "images.txt", testFilenames );
+    if( testFilenames.empty() )
+    {
+        cout << "Can not read no one test images" << endl;
+        return -1;
+    }
+
+    // 2. Detect objects
+    DOTDetector::DetectParams detectParams;
+    detectParams.minRatio = 0.8f;
+    detectParams.minRegionSize = 5;
+    detectParams.maxRegionSize = 11;
+#if SHOW_ALL_RECTS_BY_ONE
+    detectParams.isGroup = false;
+#endif
+
+    for( size_t imgIdx = 0; imgIdx < testFilenames.size(); imgIdx++ )
+    {
+        string curFilename = testDirName + testFilenames[imgIdx];
+        cout << curFilename << endl;
+        Mat queryImage = imread( curFilename, 0 );
+
+        if( queryImage.empty() )
+            continue;
+
+        cout << "Detection start ..." << endl;
+        vector<vector<Rect> > rects;
+        vector<vector<float> > ratios;
+        vector<vector<int> > trainTemlateIdxs;
+        dotDetector.detectMultiScale( queryImage, rects, detectParams, &ratios, &trainTemlateIdxs );
+        cout << "end" << endl;
+
+        Mat draw;
+        cvtColor( queryImage, draw, CV_GRAY2BGR );
+
+#if SHOW_ALL_RECTS_BY_ONE
+        DOTDetector::groupRectanglesList( rects, 3, 0.2 );
+#endif
+
+        const int textStep = 25;
+        for( size_t ci = 0; ci < classNames.size(); ci++ )
+        {
+            putText( draw, classNames[ci], Point(textStep, textStep*(1+ci)), 1, 2, colors[ci], 3 );
+            for( size_t ri = 0; ri < rects[ci].size(); ri++ )
+            {
+                rectangle( draw, rects[ci][ri], colors[ci], 3 );
+            }
+        }
+        Mat scaledDraw;
+        cv::resize( draw, scaledDraw, Size(640, 480) );
+        imshow( "detection result", scaledDraw );
+
+        cv::waitKey();
+    }
+
+}