noise control implementation started

noise control implementation ended

noise control errors fixed (part I)

noise control errors fixed (ended)

Perf Test for staticSaliencySpectralResidual and objectnessBING added

improvements and bugs fixed

saliency perf test added

matrices optimization

Where possible, CV_8U matrices have been used in place of CV_32F

new sorting algorithm implemented (first part)

new sorting algorithm implemented (second part)

new sorting algorithm implemented (finished)

modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp

@@ -234,15 +234,28 @@ private:
   bool templateOrdering();
   bool templateReplacement( const Mat& finalBFMask, const Mat& image );
 
+  // Decision threshold adaptation and Activity control function
+  bool activityControl(const Mat& current_noisePixelsMask);
+  bool decisionThresholdAdaptation();
+
   // changing structure
   std::vector<Ptr<Mat> > backgroundModel;// The vector represents the background template T0---TK of reference paper.
   // Matrices are two-channel matrix. In the first layer there are the B (background value)
   // for each pixel. In the second layer, there are the C (efficacy) value for each pixel
   Mat potentialBackground;// Two channel Matrix. For each pixel, in the first level there are the Ba value (potential background value)
                           // and in the secon level there are the Ca value, the counter for each potential value.
-  Mat epslonPixelsValue;  // epslon threshold
+  Mat epslonPixelsValue;// epslon threshold
+
+  Mat activityPixelsValue;// Activity level of each pixel
+
+  //vector<Mat> noisePixelMask; // We define a ‘noise-pixel’ as a pixel that has been classified as a foreground pixel during the full resolution
+  Mat noisePixelMask;// We define a ‘noise-pixel’ as a pixel that has been classified as a foreground pixel during the full resolution
+  //detection process,however, after the low resolution detection, it has become a
+  // background pixel. The matrix is  two-channel matrix. In the first layer there is the mask ( the identified noise-pixels are set to 1 while other pixels are 0)
+  // for each pixel. In the second layer, there is the value of activity level A for each pixel.
 
   //fixed parameter
+  bool activityControlFlag;
   bool neighborhoodCheck;
   int N_DS;// Number of template to be downsampled and used in lowResolutionDetection function
   CV_PROP_RW int imageWidth;// Width of input image
@@ -257,6 +270,13 @@ private:
             // long-term template, regardless of any subsequent background changes. A relatively large (eg gamma=3) will
             //restrain the generation of ghosts.
 
+  int Ainc;// Activity Incrementation;
+  int Bmax;// Upper-bound value for pixel activity
+  int Bth;// Max activity threshold
+  int Binc, Bdec;// Threshold for pixel-level decision threshold (epslon) adaptation
+  float deltaINC, deltaDEC;// Increment-decrement value for epslon adaptation
+  int epslonMIN, epslonMAX;// Range values for epslon threshold
+
 };
 
 /************************************ Specific Objectness Specialized Classes ************************************/
@@ -417,7 +437,7 @@ private:
   int _Clr;//
   static const char* _clrName[3];
 
-  // Names and paths to read model and to store results
+// Names and paths to read model and to store results
   std::string _modelName, _bbResDir, _trainingPath, _resultsDir;
 
   std::vector<int> _svmSzIdxs;// Indexes of active size. It's equal to _svmFilters.size() and _svmReW1f.rows
@@ -425,12 +445,12 @@ private:
   FilterTIG _tigF;// TIG filter
   Mat _svmReW1f;// Re-weight parameters learned at stage II.
 
-  // List of the rectangles' objectness value, in the same order as
-  // the  vector<Vec4i> objectnessBoundingBox returned by the algorithm (in computeSaliencyImpl function)
+// List of the rectangles' objectness value, in the same order as
+// the  vector<Vec4i> objectnessBoundingBox returned by the algorithm (in computeSaliencyImpl function)
   std::vector<float> objectnessValues;
 
 private:
-  // functions
+// functions
 
   inline static float LoG( float x, float y, float delta )
   {
@@ -438,17 +458,17 @@ private:
     return -1.0f / ( (float) ( CV_PI ) * pow( delta, 4 ) ) * ( 1 + d ) * exp( d );
   }  // Laplacian of Gaussian
 
-  // Read matrix from binary file
+// Read matrix from binary file
   static bool matRead( const std::string& filename, Mat& M );
 
   void setColorSpace( int clr = MAXBGR );
 
-  // Load trained model.
+// Load trained model.
   int loadTrainedModel( std::string modelName = "" );// Return -1, 0, or 1 if partial, none, or all loaded
 
-  // Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY).
-  // The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII().
-  // Use numDet to control the final number of proposed bounding boxes, and number of per size (scale and aspect ratio)
+// Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY).
+// The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII().
+// Use numDet to control the final number of proposed bounding boxes, and number of per size (scale and aspect ratio)
   void getObjBndBoxes( Mat &img3u, ValStructVec<float, Vec4i> &valBoxes, int numDetPerSize = 120 );
   void getObjBndBoxesForSingleImage( Mat img, ValStructVec<float, Vec4i> &boxes, int numDetPerSize );
 
@@ -460,7 +480,7 @@ private:
   void predictBBoxSI( Mat &mag3u, ValStructVec<float, Vec4i> &valBoxes, std::vector<int> &sz, int NUM_WIN_PSZ = 100, bool fast = true );
   void predictBBoxSII( ValStructVec<float, Vec4i> &valBoxes, const std::vector<int> &sz );
 
-  // Calculate the image gradient: center option as in VLFeat
+// Calculate the image gradient: center option as in VLFeat
   void gradientMag( Mat &imgBGR3u, Mat &mag1u );
 
   static void gradientRGB( Mat &bgr3u, Mat &mag1u );
@@ -479,7 +499,7 @@ private:
     return abs( u[0] - v[0] ) + abs( u[1] - v[1] ) + abs( u[2] - v[2] );
   }
 
-  //Non-maximal suppress
+//Non-maximal suppress
   static void nonMaxSup( Mat &matchCost1f, ValStructVec<float, Point> &matchCost, int NSS = 1, int maxPoint = 50, bool fast = true );
 
 };

modules/saliency/perf/perf_main.cpp

+#include "perf_precomp.hpp"
+
+CV_PERF_TEST_MAIN(saliency)

modules/saliency/perf/perf_motionSaliencyBinWangApr2014.cpp

+/*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.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, OpenCV Foundation, 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 the copyright holders 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 "perf_precomp.hpp"
+#include <fstream>
+#include <opencv2/videoio.hpp>
+
+//write sanity: ./bin/opencv_perf_saliency --perf_write_sanity=true
+//verify sanity: ./bin/opencv_perf_saliency
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+
+#define TESTSET_NAMES \
+    "/cv/saliency/motion/blizzard.webm",\
+    "/cv/saliency/motion/pedestrian.webm" ,\
+    "/cv/saliency/motion/snowFall.webm"
+
+const string SALIENCY_DIR = "cv/saliency";
+
+typedef perf::TestBaseWithParam<std::string> sal;
+
+PERF_TEST_P(sal, motionSaliencyBinWangApr2014, testing::Values(TESTSET_NAMES))
+{
+  string filename = getDataPath(GetParam());
+  int startFrame=0;
+  Mat frame;
+  Mat saliencyMap;
+  int videoSize=0;
+
+  Ptr<saliency::Saliency> saliencyAlgorithm = saliency::Saliency::create( "BinWangApr2014" );
+
+  TEST_CYCLE_N(1)
+  {
+    VideoCapture c;
+    c.open( filename);
+    videoSize=c.get( CAP_PROP_FRAME_COUNT);
+    c.set( CAP_PROP_POS_FRAMES, startFrame );
+
+    for ( int frameCounter = 0; frameCounter < videoSize; frameCounter++ )
+    {
+      c >> frame;
+
+      if( frame.empty() )
+      {
+        break;
+      }
+
+      saliencyAlgorithm.dynamicCast<saliency::MotionSaliencyBinWangApr2014>()->setImagesize( frame.cols, frame.rows );
+      saliencyAlgorithm.dynamicCast<saliency::MotionSaliencyBinWangApr2014>()->init();
+
+      if( saliencyAlgorithm->computeSaliency( frame, saliencyMap ) )
+      {
+
+      }
+      else
+      {
+        FAIL()<< "***Error in the instantiation of the saliency algorithm...***\n" << endl;
+        return;
+      }
+
+    }
+  }
+
+  SANITY_CHECK(saliencyMap);
+
+}

modules/saliency/perf/perf_objectnessBING.cpp

+/*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.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, OpenCV Foundation, 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 the copyright holders 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 "perf_precomp.hpp"
+
+//write sanity: ./bin/opencv_perf_saliency --perf_write_sanity=true
+//verify sanity: ./bin/opencv_perf_saliency
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+typedef perf::TestBaseWithParam<std::string> sal;
+
+#define BING_IMAGES \
+    "cv/saliency/objectness/000021.jpg", \
+"cv/saliency/objectness/000022.jpg"
+
+void getMatOfRects( const vector<Vec4i>& saliencyMap, Mat& bbs_mat )
+{
+  for ( size_t b = 0; b < saliencyMap.size(); b++ )
+  {
+    bbs_mat.at<int>( b, 0 ) = saliencyMap[b].val[0];
+    bbs_mat.at<int>( b, 1 ) = saliencyMap[b].val[1];
+    bbs_mat.at<int>( b, 2 ) = saliencyMap[b].val[2];
+    bbs_mat.at<int>( b, 3 ) = saliencyMap[b].val[3];
+  }
+}
+
+PERF_TEST_P(sal, objectnessBING, testing::Values(BING_IMAGES))
+{
+  string filename = getDataPath(GetParam());
+  cout<<endl<<endl<<"path "<<filename<<endl<<endl;
+  Mat image = imread(filename);
+  vector<Vec4i> saliencyMap;
+  String training_path = "/home/puja/src/opencv_contrib/modules/saliency/samples/ObjectnessTrainedModel";
+
+  if (image.empty())
+  FAIL() << "Unable to load source image " << filename;
+
+  Ptr<saliency::Saliency> saliencyAlgorithm = saliency::Saliency::create( "BING" );
+
+  TEST_CYCLE_N(1)
+  {
+    if( training_path.empty() )
+    {
+      FAIL() << "Path of trained files missing! " << endl;
+      return;
+    }
+
+    else
+    {
+      saliencyAlgorithm.dynamicCast<saliency::ObjectnessBING>()->setTrainingPath( training_path );
+      saliencyAlgorithm.dynamicCast<saliency::ObjectnessBING>()->setBBResDir( training_path + "/Results" );
+
+      if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
+      {
+
+      }
+    }
+  }  //end CYCLE
+
+  //save the bounding boxes in a Mat
+   Mat bbs_mat( saliencyMap.size(), 4, CV_32F );
+   getMatOfRects( saliencyMap, bbs_mat );
+
+   SANITY_CHECK( bbs_mat);
+}

modules/saliency/perf/perf_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_SALIENCY_PRECOMP_HPP__
+#define __OPENCV_SALIENCY_PRECOMP_HPP__
+
+#include <opencv2/ts.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/saliency.hpp>
+#include <opencv2/highgui.hpp>
+
+#ifdef GTEST_CREATE_SHARED_LIBRARY
+#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
+#endif
+
+#endif

modules/saliency/perf/perf_staticSaliencySpectralResidual.cpp

+/*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.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, OpenCV Foundation, 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 the copyright holders 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 "perf_precomp.hpp"
+
+//write sanity: ./bin/opencv_perf_saliency --perf_write_sanity=true
+//verify sanity: ./bin/opencv_perf_saliency
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+typedef perf::TestBaseWithParam<std::string> sal;
+
+#define STATIC_IMAGES \
+    "cv/saliency/static_saliency/8.jpg",\
+    "cv/saliency/static_saliency/39.jpg" ,\
+    "cv/saliency/static_saliency/41.jpg" ,\
+    "cv/saliency/static_saliency/62.jpg"
+
+PERF_TEST_P(sal, statiSaliencySpectralResidual, testing::Values(STATIC_IMAGES))
+{
+  string filename = getDataPath(GetParam());
+  Mat image = imread(filename);
+  Mat saliencyMap;
+
+  if (image.empty())
+  FAIL() << "Unable to load source image " << filename;
+
+  Ptr<saliency::Saliency> saliencyAlgorithm = saliency::Saliency::create( "SPECTRAL_RESIDUAL" );
+
+  TEST_CYCLE_N(1)
+  {
+
+    if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
+    {
+
+    }
+    else
+    {
+      FAIL()<< "***Error in the instantiation of the saliency algorithm...***\n" << endl;
+      return;
+    }
+  } //end CYCLE
+
+  SANITY_CHECK(saliencyMap);
+}

modules/saliency/samples/computeSaliency.cpp

@@ -64,6 +64,7 @@ static void help()
 
 int main( int argc, char** argv )
 {
+
   CommandLineParser parser( argc, argv, keys );
 
   String saliency_algorithm = parser.get<String>( 0 );
@@ -175,12 +176,16 @@ int main( int argc, char** argv )
       {
 
         cap >> frame;
+        if( frame.empty() )
+        {
+          return 0;
+        }
         cvtColor( frame, frame, COLOR_BGR2GRAY );
 
         Mat saliencyMap;
         if( saliencyAlgorithm->computeSaliency( frame, saliencyMap ) )
         {
-          std::cout << "current frame motion saliency done" << std::endl;
+          //std::cout << "current frame motion saliency done" << std::endl;
         }
 
         imshow( "image", frame );

modules/saliency/src/motionSaliencyBinWangApr2014.cpp

@@ -41,11 +41,10 @@
 
 #include <limits>
 #include "precomp.hpp"
-//TODO delete highgui include
-//#include <opencv2/highgui.hpp>
 
 #define thetaA_VAL 200
 #define thetaL_VAL 250
+#define epslonGeneric 20
 
 namespace cv
 {
@@ -71,19 +70,25 @@ MotionSaliencyBinWangApr2014::MotionSaliencyBinWangApr2014()
   gamma = 3;
   neighborhoodCheck = true;
 
+  Ainc = 6;  // Activity Incrementation;
+  Bmax = 80;  // Upper-bound value for pixel activity
+  Bth = 20;  //70;  // Max activity threshold
+  Binc = 15;  //50;
+  Bdec = 5;  //20;  // Threshold for pixel-level decision threshold (epslon) adaptation
+  deltaINC = 20;
+  deltaDEC = 0.125;  // Increment-decrement value for epslon adaptation
+  epslonMIN = 18;
+  epslonMAX = 80;
+
   className = "BinWangApr2014";
 }
 
 bool MotionSaliencyBinWangApr2014::init()
 {
+  activityControlFlag = false;
   Size imgSize( imageWidth, imageHeight );
-  epslonPixelsValue = Mat( imgSize.height, imgSize.width, CV_32F, Scalar( 20 ) );
-  // Median of range [18, 80] advised in reference paper.
-  // Since data is even, the median is estimated using two values ​​that occupy
-  // the position (n / 2) and ((n / 2) +1) (choose their arithmetic mean).
-
-  potentialBackground = Mat( imgSize.height, imgSize.width, CV_32FC2, Scalar( std::numeric_limits<float>::quiet_NaN(), 0 ) );
-
+  epslonPixelsValue = Mat( imgSize.height, imgSize.width, CV_32F, Scalar( epslonGeneric ) );
+  potentialBackground = Mat( imgSize.height, imgSize.width, CV_8UC2, Scalar( 0, 0 ) );
   backgroundModel.resize( K + 1 );
 
   for ( int i = 0; i < K + 1; i++ )
@@ -95,6 +100,11 @@ bool MotionSaliencyBinWangApr2014::init()
     backgroundModel[i] = tmp;
   }
 
+  noisePixelMask.create( imgSize.height, imgSize.width, CV_8U );
+  noisePixelMask.setTo( Scalar( 0 ) );
+  activityPixelsValue.create( imgSize.height, imgSize.width, CV_8U );
+  activityPixelsValue.setTo( Scalar( 0 ) );
+
   return true;
 
 }
@@ -109,17 +119,17 @@ bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, M
 {
   Mat image = image2.clone();
 
-  float currentPixelValue;
+  uchar currentPixelValue;
   float currentEpslonValue;
   bool backgFlag = false;
 
   // Initially, all pixels are considered as foreground and then we evaluate with the background model
-  highResBFMask.create( image.rows, image.cols, CV_32F );
+  highResBFMask.create( image.rows, image.cols, CV_8U );
   highResBFMask.setTo( 1 );
 
   uchar* pImage;
   float* pEpslon;
-  float* pMask;
+  uchar* pMask;
 
   // Scan all pixels of image
   for ( int i = 0; i < image.rows; i++ )
@@ -127,61 +137,72 @@ bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, M
 
     pImage = image.ptr<uchar>( i );
     pEpslon = epslonPixelsValue.ptr<float>( i );
-    pMask = highResBFMask.ptr<float>( i );
+    pMask = highResBFMask.ptr<uchar>( i );
     for ( int j = 0; j < image.cols; j++ )
     {
-
-      backgFlag = false;
-      currentPixelValue = pImage[j];
-      currentEpslonValue = pEpslon[j];
-
-      int counter = 0;
-      for ( size_t z = 0; z < backgroundModel.size(); z++ )
+      /*    Pixels with activity greater than Bth are eliminated from the detection result. In this way,
+       continuously blinking noise-pixels will be eliminated from the detection results,
+       preventing the generation of false positives.*/
+      if( activityPixelsValue.at<uchar>( i, j ) < Bth )
       {
+        backgFlag = false;
+        currentPixelValue = pImage[j];
+        currentEpslonValue = pEpslon[j];
 
-        counter += (int) backgroundModel[z]->ptr<Vec2f>( i )[j][1];
-      }
+        int counter = 0;
+        for ( size_t z = 0; z < backgroundModel.size(); z++ )
+        {
 
-      if( counter != 0 )  //if at least the first template is activated / initialized
-      {
+          counter += (int) backgroundModel[z]->ptr<Vec2f>( i )[j][1];
+          if( counter != 0 )
+            break;
+        }
 
-        // scan background model vector
-        for ( size_t z = 0; z < backgroundModel.size(); z++ )
+        if( counter != 0 )  //if at least the first template is activated / initialized
         {
-          float* currentB;
-          float* currentC;
-          currentB = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][0] );
-          currentC = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][1] );
 
-          //continue;
-          if( ( *currentC ) > 0 )  //The current template is active
+          // scan background model vector
+          for ( size_t z = 0; z < backgroundModel.size(); z++ )
           {
-            // If there is a match with a current background template
-            if( abs( currentPixelValue - ( *currentB ) ) < currentEpslonValue && !backgFlag )
+            float* currentB;
+            float* currentC;
+            currentB = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][0] );
+            currentC = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][1] );
+
+            //continue;
+            if( ( *currentC ) > 0 )  //The current template is active
             {
-              // The correspondence pixel in the  BF mask is set as background ( 0 value)
-              pMask[j] = 0;
-              if( ( *currentC < L0 && z == 0 ) || ( *currentC < L1 && z == 1 ) || ( z > 1 ) )
+              // If there is a match with a current background template
+              if( abs( currentPixelValue - ( *currentB ) ) < currentEpslonValue && !backgFlag )
               {
-                *currentC += 1;  // increment the efficacy of this template
+                // The correspondence pixel in the  BF mask is set as background ( 0 value)
+                pMask[j] = 0;
+                if( ( *currentC < L0 && z == 0 ) || ( *currentC < L1 && z == 1 ) || ( z > 1 ) )
+                {
+                  *currentC += 1;  // increment the efficacy of this template
+                }
+
+                *currentB = ( ( 1 - alpha ) * ( *currentB ) ) + ( alpha * currentPixelValue );  // Update the template value
+                backgFlag = true;
+              }
+              else
+              {
+                *currentC -= 1;  // decrement the efficacy of this template
               }
 
-              *currentB = ( ( 1 - alpha ) * ( *currentB ) ) + ( alpha * currentPixelValue );  // Update the template value
-              backgFlag = true;
-            }
-            else
-            {
-              *currentC -= 1;  // decrement the efficacy of this template
             }
 
-          }
-
-        }  // end "for" cicle of template vector
+          }  // end "for" cicle of template vector
 
+        }
+        else
+        {
+          pMask[j] = 1;  //if the model of the current pixel is not yet initialized, we mark the pixels as foreground
+        }
       }
       else
       {
-        pMask[j] = 1;  //if the model of the current pixel is not yet initialized, we mark the pixels as foreground
+        pMask[j] = 0;
       }
 
     }
@@ -211,7 +232,7 @@ bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat
     Mat currentModel;
 
     // Initially, all pixels are considered as foreground and then we evaluate with the background model
-    lowResBFMask.create( image.rows, image.cols, CV_32F );
+    lowResBFMask.create( image.rows, image.cols, CV_8U );
     lowResBFMask.setTo( 1 );
 
     // Scan all the ROI of original matrices
@@ -225,41 +246,53 @@ bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat
 
       for ( int j = 0; j < ceil( (float) image.cols / N ); j++ )
       {
-        // Compute the mean of image's block and epslonMatrix's block based on ROI
-        Mat roiImage = image( roi );
-        Mat roiEpslon = epslonPixelsValue( roi );
-        currentPixelValue = (float) mean( roiImage ).val[0];
-        currentEpslonValue = (float) mean( roiEpslon ).val[0];
-
-        // scan background model vector
-        for ( int z = 0; z < N_DS; z++ )
+        /* Pixels with activity greater than Bth are eliminated from the detection result. In this way,
+         continuously blinking noise-pixels will be eliminated from the detection results,
+         preventing the generation of false positives.*/
+        if( activityPixelsValue.at<uchar>( i, j ) < Bth )
         {
-          // Select the current template 2 channel matrix, select ROI and compute the mean for each channel separately
-          Mat roiTemplate = ( * ( backgroundModel[z] ) )( roi );
-          Scalar templateMean = mean( roiTemplate );
-          currentB = (float) templateMean[0];
-          currentC = (float) templateMean[1];
 
-          if( ( currentC ) > 0 )  //The current template is active
+          // Compute the mean of image's block and epslonMatrix's block based on ROI
+          Mat roiImage = image( roi );
+          Mat roiEpslon = epslonPixelsValue( roi );
+          currentPixelValue = (float) mean( roiImage ).val[0];
+          currentEpslonValue = (float) mean( roiEpslon ).val[0];
+
+          // scan background model vector
+          for ( int z = 0; z < N_DS; z++ )
           {
-            // If there is a match with a current background template
-            if( abs( currentPixelValue - ( currentB ) ) < currentEpslonValue )
+            // Select the current template 2 channel matrix, select ROI and compute the mean for each channel separately
+            Mat roiTemplate = ( * ( backgroundModel[z] ) )( roi );
+            Scalar templateMean = mean( roiTemplate );
+            currentB = (float) templateMean[0];
+            currentC = (float) templateMean[1];
+
+            if( ( currentC ) > 0 )  //The current template is active
             {
-              // The correspondence pixel in the  BF mask is set as background ( 0 value)
-              rectangle( lowResBFMask, roi, Scalar( 0 ), FILLED );
-              break;
+              // If there is a match with a current background template
+              if( abs( currentPixelValue - ( currentB ) ) < currentEpslonValue )
+              {
+                // The correspondence pixel in the  BF mask is set as background ( 0 value)
+                rectangle( lowResBFMask, roi, Scalar( 0 ), FILLED );
+                break;
+              }
             }
           }
+          // Shift the ROI from left to right follow the block dimension
+          roi = roi + Point( N, 0 );
+          if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) <= ( image.rows - 1 ) )
+          {
+            roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, N ) );
+          }
+          else if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) > ( image.rows - 1 ) )
+          {
+            roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, abs( ( image.rows - 1 ) - roi.y ) + 1 ) );
+          }
         }
-        // Shift the ROI from left to right follow the block dimension
-        roi = roi + Point( N, 0 );
-        if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) <= ( image.rows - 1 ) )
-        {
-          roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, N ) );
-        }
-        else if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) > ( image.rows - 1 ) )
+        else
         {
-          roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, abs( ( image.rows - 1 ) - roi.y ) + 1 ) );
+          // The correspondence pixel in the  BF mask is set as background ( 0 value)
+          rectangle( lowResBFMask, roi, Scalar( 0 ), FILLED );
         }
       }
       //Shift the ROI from up to down follow the block dimension, also bringing it back to beginning of row
@@ -275,7 +308,7 @@ bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat
   }
   else
   {
-    lowResBFMask.create( image.rows, image.cols, CV_32F );
+    lowResBFMask.create( image.rows, image.cols, CV_8U );
     lowResBFMask.setTo( 1 );
     return false;
   }
@@ -289,58 +322,70 @@ bool inline pairCompare( std::pair<float, float> t, std::pair<float, float> t_pl
 
 }
 
-// Background model maintenance functions
 bool MotionSaliencyBinWangApr2014::templateOrdering()
 {
-  std::vector<std::pair<float, float> > pixelTemplates( backgroundModel.size() );
 
-  Vec2f* bgModel_0P;
-  Vec2f* bgModel_1P;
+  Mat dstMask, tempMat, dstMask2, dstMask3;
+  Mat convertMat1, convertMat2;
+  int backGroundModelSize = backgroundModel.size();
+
+  std::vector<std::vector<Mat> > channelSplit( backGroundModelSize );
+  for ( int i = 0; i < backGroundModelSize; i++ )
+  {
+    split( *backgroundModel[i], channelSplit[i] );
+
+  }
 
-// Scan all pixels of image
-  for ( int i = 0; i < backgroundModel[0]->rows; i++ )
+  //Bubble sort : Template T1 - Tk
+  for ( int i = 1; i < backGroundModelSize - 1; i++ )
   {
-    bgModel_0P = backgroundModel[0]->ptr<Vec2f>( i );
-    bgModel_1P = backgroundModel[1]->ptr<Vec2f>( i );
-    for ( int j = 0; j < backgroundModel[0]->cols; j++ )
+    // compare and order the i-th template with the others
+    for ( int j = i + 1; j < backGroundModelSize; j++ )
     {
-      // scan background model vector from T1 to Tk
-      for ( size_t z = 1; z < backgroundModel.size(); z++ )
-      {
-        Vec2f bgModel_zP = backgroundModel[z]->ptr<Vec2f>( i )[j];
-        // Fill vector of pairs
-        pixelTemplates[z - 1].first = bgModel_zP[0];  // Current B (background value)
-        pixelTemplates[z - 1].second = bgModel_zP[1];  // Current C (efficacy value)
-      }
 
-      //SORT template from T1 to Tk
-      std::sort( pixelTemplates.begin(), pixelTemplates.end(), pairCompare );
+      compare( channelSplit[j][1], channelSplit[i][1], dstMask, CMP_GT );
 
-      //REFILL CURRENT MODEL ( T1...Tk)
-      for ( size_t zz = 1; zz < backgroundModel.size(); zz++ )
-      {
-        backgroundModel[zz]->ptr<Vec2f>( i )[j][0] = pixelTemplates[zz - 1].first;  // Replace previous B with new ordered B value
-        backgroundModel[zz]->ptr<Vec2f>( i )[j][1] = pixelTemplates[zz - 1].second;  // Replace previous C with new ordered C value
-      }
+      channelSplit[i][0].copyTo( tempMat );
+      channelSplit[j][0].copyTo( channelSplit[i][0], dstMask );
+      tempMat.copyTo( channelSplit[j][0], dstMask );
 
-      // SORT Template T0 and T1
-      if( bgModel_1P[j][1] > thetaL && bgModel_0P[j][1] < thetaL )
-      {
+      channelSplit[i][1].copyTo( tempMat );
+      channelSplit[j][1].copyTo( channelSplit[i][1], dstMask );
+      tempMat.copyTo( channelSplit[j][1], dstMask );
+    }
+  }
 
-        // swap B value of T0 with B value of T1 (for current model)
-        swap( bgModel_0P[j][0], bgModel_1P[j][0] );
+  // SORT Template T0 and T1
+  Mat M_deltaL( backgroundModel[0]->rows, backgroundModel[0]->cols, CV_32F, Scalar( thetaL ) );
 
-        // set new C0 value for current model)
-        swap( bgModel_0P[j][1], bgModel_1P[j][1] );
-        bgModel_0P[j][1] = (float) gamma * thetaL;
+  compare( channelSplit[1][1], M_deltaL, dstMask2, CMP_GT );
+  compare( M_deltaL, channelSplit[0][1], dstMask3, CMP_GT );
 
-      }
+  threshold( dstMask2, dstMask2, 0, 1, THRESH_BINARY );
+  threshold( dstMask3, dstMask3, 0, 1, THRESH_BINARY );
 
-    }
+  bitwise_and( dstMask2, dstMask3, dstMask );
+
+  //copy correct B element of T1 inside T0 and swap
+  channelSplit[0][0].copyTo( tempMat );
+  channelSplit[1][0].copyTo( channelSplit[0][0], dstMask );
+  tempMat.copyTo( channelSplit[1][0], dstMask );
+
+  //copy correct C element of T0 inside T1
+  channelSplit[0][1].copyTo( channelSplit[1][1], dstMask );
+
+  //set new C0 values as gamma * thetaL
+  M_deltaL.mul( gamma );
+  M_deltaL.copyTo( channelSplit[0][1], dstMask );
+
+  for ( int i = 0; i < backGroundModelSize; i++ )
+  {
+    merge( channelSplit[i], *backgroundModel[i] );
   }
 
   return true;
 }
+
 bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask, const Mat& image )
 {
   std::vector<Mat> temp;
@@ -351,6 +396,8 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
   {
     thetaA = 50;
     thetaL = 150;
+    /*    thetaA = 5;
+     thetaL = 15;*/
     neighborhoodCheck = false;
 
   }
@@ -364,19 +411,19 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
   int roiSize = 3;  // FIXED ROI SIZE, not change until you first appropriately adjust the following controls in the EVALUATION section!
   int countNonZeroElements = 0;
   std::vector<Mat> mv;
-  Mat replicateCurrentBAMat( roiSize, roiSize, CV_32F );
+  Mat replicateCurrentBAMat( roiSize, roiSize, CV_8U );
   Mat backgroundModelROI( roiSize, roiSize, CV_32F );
-  Mat diffResult( roiSize, roiSize, CV_32F );
+  Mat diffResult( roiSize, roiSize, CV_8U );
 
 // Scan all pixels of finalBFMask and all pixels of others models (the dimension are the same)
-  const float* finalBFMaskP;
-  Vec2f* pbgP;
+  const uchar* finalBFMaskP;
+  Vec2b* pbgP;
   const uchar* imageP;
   float* epslonP;
   for ( int i = 0; i < finalBFMask.rows; i++ )
   {
-    finalBFMaskP = finalBFMask.ptr<float>( i );
-    pbgP = potentialBackground.ptr<Vec2f>( i );
+    finalBFMaskP = finalBFMask.ptr<uchar>( i );
+    pbgP = potentialBackground.ptr<Vec2b>( i );
     imageP = image.ptr<uchar>( i );
     epslonP = epslonPixelsValue.ptr<float>( i );
     for ( int j = 0; j < finalBFMask.cols; j++ )
@@ -388,7 +435,7 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
          * will be loaded into BA and CA will be set to 1*/
         if( pbgP[j][1] == 0 )
         {
-          pbgP[j][0] = (float) imageP[j];
+          pbgP[j][0] = imageP[j];
           pbgP[j][1] = 1;
         }
 
@@ -471,6 +518,8 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
               resize( replicateCurrentBAMat, replicateCurrentBAMat, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
               resize( diffResult, diffResult, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
 
+              backgroundModelROI.convertTo( backgroundModelROI, CV_8U );
+
               absdiff( replicateCurrentBAMat, backgroundModelROI, diffResult );
               threshold( diffResult, diffResult, epslonP[j], 255, THRESH_BINARY_INV );
               countNonZeroElements = countNonZero( diffResult );
@@ -479,9 +528,9 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
               {
                 /////////////////// REPLACEMENT of backgroundModel template ///////////////////
                 //replace TA with current TK
-                backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
-                potentialBackground.at<Vec2f>( i, j )[0] = std::numeric_limits<float>::quiet_NaN();
-                potentialBackground.at<Vec2f>( i, j )[1] = 0;
+                backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2b>( i, j );
+                potentialBackground.at<Vec2b>( i, j )[0] = 0;
+                potentialBackground.at<Vec2b>( i, j )[1] = 0;
 
                 break;
               }
@@ -489,9 +538,9 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
           }
           else
           {
-            backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
-            potentialBackground.at<Vec2f>( i, j )[0] = std::numeric_limits<float>::quiet_NaN();
-            potentialBackground.at<Vec2f>( i, j )[1] = 0;
+            backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2b>( i, j );
+            potentialBackground.at<Vec2b>( i, j )[0] = 0;
+            potentialBackground.at<Vec2b>( i, j )[1] = 0;
           }
         }  // close if of EVALUATION
       }  // end of  if( finalBFMask.at<uchar>( i, j ) == 1 )  // i.e. the corresponding frame pixel has been market as foreground
@@ -502,25 +551,119 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
   return true;
 }
 
-bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( InputArray image, OutputArray saliencyMap )
+bool MotionSaliencyBinWangApr2014::activityControl( const Mat& current_noisePixelsMask )
+{
+  Mat discordanceFramesNoise, not_current_noisePixelsMask;
+  Mat nonZeroIndexes, not_discordanceFramesNoise;  //u_current_noisePixelsMask;
+
+//current_noisePixelsMask.convertTo( u_current_noisePixelsMask, CV_8UC1 );
+
+// Derive the discrepancy between noise in the frame n-1 and frame n
+//threshold( u_current_noisePixelsMask, not_current_noisePixelsMask, 0.5, 1.0, THRESH_BINARY_INV );
+  threshold( current_noisePixelsMask, not_current_noisePixelsMask, 0.5, 1.0, THRESH_BINARY_INV );
+  bitwise_and( noisePixelMask, not_current_noisePixelsMask, discordanceFramesNoise );
+
+// indices in which the pixel at frame n-1 was the noise (or not) and now no (or yes) (blinking pixels)
+  findNonZero( discordanceFramesNoise, nonZeroIndexes );
+
+  Vec2i temp;
+
+// we increase the activity value of these pixels
+  for ( int i = 0; i < nonZeroIndexes.rows; i++ )
+  {
+    //TODO check rows, cols inside at
+    temp = nonZeroIndexes.at<Vec2i>( i );
+    if( activityPixelsValue.at<uchar>( temp.val[1], temp.val[0] ) < Bmax )
+    {
+      activityPixelsValue.at<uchar>( temp.val[1], temp.val[0] ) += Ainc;
+    }
+  }
+
+// decrement other pixels that have not changed (not blinking)
+  threshold( discordanceFramesNoise, not_discordanceFramesNoise, 0.5, 1.0, THRESH_BINARY_INV );
+  findNonZero( not_discordanceFramesNoise, nonZeroIndexes );
+
+  Vec2i temp2;
+
+  for ( int j = 0; j < nonZeroIndexes.rows; j++ )
+  {
+    temp2 = nonZeroIndexes.at<Vec2i>( j );
+    if( activityPixelsValue.at<uchar>( temp2.val[1], temp2.val[0] ) > 0 )
+    {
+      activityPixelsValue.at<uchar>( temp2.val[1], temp2.val[0] ) -= 1;
+    }
+  }
+// update the noisePixelsMask
+  current_noisePixelsMask.copyTo( noisePixelMask );
+
+  return true;
+}
+
+bool MotionSaliencyBinWangApr2014::decisionThresholdAdaptation()
 {
-  Mat highResBFMask;
-  Mat lowResBFMask;
+
+  for ( int i = 0; i < activityPixelsValue.rows; i++ )
+  {
+    for ( int j = 0; j < activityPixelsValue.cols; j++ )
+    {
+      if( activityPixelsValue.at<uchar>( i, j ) > Binc && ( epslonPixelsValue.at<float>( i, j ) + deltaINC ) < epslonMAX )
+      {
+
+        epslonPixelsValue.at<float>( i, j ) += deltaINC;
+      }
+      else if( activityPixelsValue.at<uchar>( i, j ) < Bdec && ( epslonPixelsValue.at<float>( i, j ) - deltaDEC ) > epslonMIN )
+      {
+        epslonPixelsValue.at<float>( i, j ) -= deltaDEC;
+      }
+    }
+  }
+
+  return true;
+}
+
+bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( const InputArray image, OutputArray saliencyMap )
+{
+  Mat highResBFMask, u_highResBFMask;
+  Mat lowResBFMask, u_lowResBFMask;
   Mat not_lowResBFMask;
-  Mat noisePixelsMask;
+  Mat current_noisePixelsMask;
 
   fullResolutionDetection( image.getMat(), highResBFMask );
   lowResolutionDetection( image.getMat(), lowResBFMask );
 
-
 // Compute the final background-foreground mask. One pixel is marked as foreground if and only if it is
 // foreground in both masks (full and low)
   bitwise_and( highResBFMask, lowResBFMask, saliencyMap );
 
+  if( activityControlFlag )
+  {
+
+// Detect the noise pixels (i.e. for a given pixel, fullRes(pixel) = foreground and lowRes(pixel)= background)
+    threshold( lowResBFMask, not_lowResBFMask, 0.5, 1.0, THRESH_BINARY_INV );
+    bitwise_and( highResBFMask, not_lowResBFMask, current_noisePixelsMask );
+
+    activityControl( current_noisePixelsMask );
+    decisionThresholdAdaptation();
+  }
+
+  //double t = (double) getTickCount();
+
   templateOrdering();
+
+  /*t = ( (double) getTickCount() - t ) / getTickFrequency();
+   std::cout << "T :" << t << std::endl;
+   */
   templateReplacement( saliencyMap.getMat(), image.getMat() );
+
+  //double t2 = (double) getTickCount();
+
   templateOrdering();
 
+  //t2 = ( (double) getTickCount() - t2 ) / getTickFrequency();
+  //std::cout << "T2 :" << t2 << std::endl;
+
+  activityControlFlag = true;
+
   return true;
 }