diff --git a/src/pointbase.cpp b/src/pointbase.cpp
index b7fc26f..5861af1 100644
--- a/src/pointbase.cpp
+++ b/src/pointbase.cpp
@@ -519,13 +519,14 @@ class PointBase {
                     if (xLoc + xWitdth > windowWidth) xWitdth = windowWidth - xLoc;
                     if (yLoc + yHeight > windowHeight) yHeight = windowHeight - yLoc;
 
+                    //create the ROI
                     Mat roi;
                     roi = img(Rect(xLoc, yLoc, xWitdth, yHeight));
                     /*imshow("roi",roi);
                     imwrite("temp.jpg", roi);
                     waitKey(0);*/
 
-                    
+                    //detect the keypoints from both the ROI and the img we want to add
                     cv::Ptr<SIFT> siftPtr = SIFT::create();
                     std::vector<KeyPoint> keypointsROI, keypointsImg;
                     cv::Ptr<SiftDescriptorExtractor> siftExtrPtr;
@@ -535,13 +536,7 @@ class PointBase {
 
 
 
-                    // Add results to image and save.
-                    /*
-                    cv::Mat output;
-                    cv::drawKeypoints(imgSmall, keypointsImg, output);
-                    imshow("sift_result.jpg", output);
-                    waitKey(0);*/
-
+                    //compute descriptors from found keypoints
                     cout<<" SIZE \n"<<cv::typeToString(roi.type()) <<" \n";
                     siftPtr->compute(roi,
                                 keypointsROI,
@@ -550,6 +545,7 @@ class PointBase {
                                 keypointsImg,
                                 descriptorsImg);
 
+                    //match the descriptors
                     cv::FlannBasedMatcher matcher;
                     std::vector<cv::DMatch> matches;
                     
@@ -567,33 +563,38 @@ class PointBase {
 
 
                     double max_dist = 0; double min_dist = 100;
-                    cout<<"ROWS DESC:"<<descriptorsImg.rows<<"\n";
-                    cout<<"SIZE DESC:"<<descriptorsImg.size()<<"\n";
-                    //-- Quick calculation of max and min distances between keypoints
+                    //cout<<"ROWS DESC:"<<descriptorsImg.rows<<"\n";
+                    //cout<<"SIZE DESC:"<<descriptorsImg.size()<<"\n";
+
+                    //Quick calculation of max and min distances between keypoints
                     for( int i = 0; i < descriptorsImg.rows; i++ )
-                    { double dist = matches[i].distance;
-                    cout<<"DIST:"<<dist<<"\n";
-                    if( dist < min_dist ) min_dist = dist;
-                    if( dist > max_dist ) max_dist = dist;
+                    { 
+                        double dist = matches[i].distance;
+                        cout<<"DIST:"<<dist<<"\n";
+                        if( dist < min_dist ) min_dist = dist;
+                        if( dist > max_dist ) max_dist = dist;
                     }
                     printf("-- Max dist : %f \n", max_dist );
                     printf("-- Min dist : %f \n", min_dist );
                     
-                     std::vector< DMatch > good_matches;
- 
+                    //find the GOOD matches from all descriptor matches
+                    //e.g. the ones, that have a distance LESS than 3* the smallest computed distance
+                    std::vector< DMatch > good_matches;
                     for( int i = 0; i < descriptorsImg.rows; i++ )
-                    { if( matches[i].distance < 3*min_dist )
-                    { good_matches.push_back( matches[i]);  }
+                    { 
+                        if( matches[i].distance < 3*min_dist ) { good_matches.push_back( matches[i]);  }
 
                     }
+
+
                     std::vector< Point2d > obj;
                     std::vector< Point2d > scene;
                     
                     for( int i = 0; i < good_matches.size(); i++ )
                     {
-                    //-- Get the keypoints from the good matches
-                    obj.push_back( static_cast<cv::Point2i>( keypointsImg[ good_matches[i].queryIdx ].pt ));
-                    scene.push_back(  static_cast<cv::Point2i>(keypointsROI[ good_matches[i].trainIdx ].pt ));
+                    //Get the keypoints from the good matches
+                    obj.push_back( static_cast<cv::Point2i>( keypointsImg[ good_matches[i].trainIdx ].pt ));
+                    scene.push_back(  static_cast<cv::Point2i>(keypointsROI[ good_matches[i].queryIdx ].pt ));
                     }
 
                     /*cv::Mat imageMatches2;
@@ -602,18 +603,84 @@ class PointBase {
                     cv::imshow("good_matches",imageMatches2);
                     cv::waitKey(0);*/
 
-
+                    //find a transformation based on good matches
+                    //we do not need a Homography, since we deal with affine transformations (no viewport transf. are expected)
                     //Mat H = findHomography( Mat(obj), Mat(scene), RANSAC );
                     Mat H = estimateAffinePartial2D( Mat(obj), Mat(scene), noArray(),RANSAC );
 
-                    cv::Mat result;
+                    cv::Mat resultWarp;
+                    cv::Mat resultBlend,roiMask,imgWarpedMask,overlapMask,overlapDST,roiDT, imgDT;
                     //warpPerspective(imgSmall,roi,H,cv::Size(roi.cols,roi.rows));
-                    warpAffine(imgSmall,result,H,cv::Size(roi.cols,roi.rows));
+                    warpAffine(imgSmall,resultWarp,H,cv::Size(roi.cols,roi.rows));
                     /*cv::Mat half(result,cv::Rect(0,0,imgSmall.cols,imgSmall.rows));
                     result.copyTo(roi);*/
                     imshow("imgSmall", imgSmall);
-                    imshow( "Result", result );
+                    imshow( "resultWarp", resultWarp );
                     imshow("roi", roi);
+                    cv::waitKey(0);
+
+                    
+                    //cv::addWeighted( roi, 0.5, resultWarp, 0.5, 0.0, resultBlend);
+
+                    //thresholding roi and img, in order to create image masks
+                    threshold(roi,roiMask,1,255,CV_8U);
+                    threshold(resultWarp,imgWarpedMask,1,255,CV_8U);
+
+                    multiply(roiMask,imgWarpedMask,overlapMask);
+                    //we need to change type in order for img multiplication to work in OpenCV
+                    cv::cvtColor(overlapMask,overlapMask,COLOR_BGR2GRAY);
+                    cv::cvtColor(roiMask,roiMask,COLOR_BGR2GRAY);
+                    cv::cvtColor(imgWarpedMask,imgWarpedMask,COLOR_BGR2GRAY);
+                    cv::cvtColor(roi,roi,COLOR_BGR2GRAY);
+                    cv::cvtColor(resultWarp,resultWarp,COLOR_BGR2GRAY);
+
+                    //the blending function is DistanceTransform(img1)/(DT(img1)+DT(img2)) - DT is created from the masks
+                    distanceTransform(roiMask,roiDT,DIST_L2, 3);
+                    distanceTransform(imgWarpedMask,imgDT,DIST_L2, 3);
+                    //normalize(overlapDST, overlapDST, 0.0, 1.0, NORM_MINMAX);
+                    
+                    
+                    cout<<cv::typeToString(roiDT.type())<< "ROI TYPE \n";
+                    cout<<cv::typeToString(imgDT.type())<< "img TYPE \n";
+                    //in order for imshow to work, we need to normalize into 0-1 range
+                    normalize(roiDT, roiDT, 0.0, 1.0, NORM_MINMAX);
+                    normalize(imgDT, imgDT, 0.0, 1.0, NORM_MINMAX);
+
+                    Mat roiOverlapAlpha, imgOverlapAlpha, resultRoi; 
+
+                    //blended images of ROI and img, now we need to only add them together 
+                    cv::divide(roiDT,(roiDT+imgDT),roiOverlapAlpha);  
+                    cv::divide(imgDT,(roiDT+imgDT),imgOverlapAlpha);  
+                    
+
+                    //imshow("roiMask", roiMask);
+                    //imshow("imgOverlapAlpha", imgOverlapAlpha);
+                    
+                    roi.convertTo(roi,CV_32FC1);
+                    resultWarp.convertTo(resultWarp,CV_32FC1);
+                    cout<<cv::typeToString(resultWarp.type())<< "resultWarp type \n";
+                    cout<<cv::typeToString(roi.type())<< "roi type \n";
+                    
+                    multiply(roiOverlapAlpha,roi,roiOverlapAlpha);
+                    multiply(imgOverlapAlpha,resultWarp,imgOverlapAlpha);
+                    normalize(imgOverlapAlpha, imgOverlapAlpha, 0.0, 1.0, NORM_MINMAX);
+                    normalize(roiOverlapAlpha, roiOverlapAlpha, 0.0, 1.0, NORM_MINMAX);
+                    imshow("imgOverlapAlpha", imgOverlapAlpha);
+                     cout<<"AAAAAAAAAAAAA \n";
+                    cv::add(roiOverlapAlpha,imgOverlapAlpha, resultRoi);
+                    
+                    normalize(resultRoi, resultRoi, 0.0, 1.0, NORM_MINMAX);
+                    imshow("resultRoi", resultRoi);
+                    cv::waitKey(0); 
+
+                    //cv::cvtColor(roiMask,roiMask,COLOR_GRAY2BGR);
+                    //cout<<cv::typeToString(roiMask.type())<< "img TYPE \n";
+                    
+                    //multiply(roiOverlapAlpha,roiMask,roiOverlapAlpha);
+                    
+             
+
+
                     cv::waitKey(0);
                     
                    //cout<<CV_VERSION;