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Using your first image(processed image) I just processed some more step like

• Threshold binary inverted
• Contour processing and finding biggest contour.
• Convexhull or drawcontours as your requirement

made the result as shown below.

Code:- Here is the C++ code for the above steps,

Mat tmp,thr;
Mat src=imread("river.jpg",1); //Your processed  image

cvtColor(src,tmp,CV_BGR2GRAY);
threshold(tmp,thr,10,255,THRESH_BINARY_INV);

    //Find biggest contour to segment exact object
    vector< vector <Point> > contours; // Vector for storing contour
    vector< Vec4i > hierarchy;
    int largest_contour_index=0;
    int largest_area=0;
    Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0)); //create destination image

    findContours( thr, contours, hierarchy,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
     for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
      {
       double a=contourArea( contours[i],false);  //  Find the area of contour
       if(a>largest_area){
       largest_area=a;
       largest_contour_index=i;                //Store the index of largest contour
       }
      }

     drawContours( dst,contours, largest_contour_index, Scalar(255,255,255),CV_FILLED, 8, hierarchy ); // Draw the largest contour using previously stored index.

     ///Either directly draw the contour
     drawContours( src,contours, largest_contour_index, Scalar(0,255,0),3, 8, hierarchy ); 

     ///Or use convex hull to draw the contour
     vector<vector<Point> >hull(1);
     convexHull(contours[largest_contour_index],  hull[0],false,true );
     drawContours( src, hull, 0, Scalar(0,0,255), 2, 8, vector<Vec4i>(), 0, Point() );
     imshow("contour",dst);
     imshow("src",src);

Result:- Red boundary is result of convexhull and Green boundary is the result of drawcontour using largest contour.

Using your first image(processed image) I just processed proceed some more step like

• Threshold binary inverted
• Contour processing and finding biggest contour.
• Convexhull or drawcontours as your requirement

made the result as shown below.

Code:- Here is the C++ code for the above steps,

Mat tmp,thr;
Mat src=imread("river.jpg",1); //Your processed  image

cvtColor(src,tmp,CV_BGR2GRAY);
threshold(tmp,thr,10,255,THRESH_BINARY_INV);

    //Find biggest contour to segment exact object
    vector< vector <Point> > contours; // Vector for storing contour
    vector< Vec4i > hierarchy;
    int largest_contour_index=0;
    int largest_area=0;
    Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0)); //create destination image

    findContours( thr, contours, hierarchy,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
     for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
      {
       double a=contourArea( contours[i],false);  //  Find the area of contour
       if(a>largest_area){
       largest_area=a;
       largest_contour_index=i;                //Store the index of largest contour
       }
      }

     drawContours( dst,contours, largest_contour_index, Scalar(255,255,255),CV_FILLED, 8, hierarchy ); // Draw the largest contour using previously stored index.

     ///Either directly draw the contour
     drawContours( src,contours, largest_contour_index, Scalar(0,255,0),3, 8, hierarchy ); 

     ///Or use convex hull to draw the contour
     vector<vector<Point> >hull(1);
     convexHull(contours[largest_contour_index],  hull[0],false,true );
     drawContours( src, hull, 0, Scalar(0,0,255), 2, 8, vector<Vec4i>(), 0, Point() );
     imshow("contour",dst);
     imshow("src",src);

Result:- Red boundary is result of convexhull and Green boundary is the result of drawcontour using largest contour.

Using your first image(processed image) I just proceed some more step like

• Threshold binary inverted
• Contour processing and finding biggest contour.
• Convexhull or drawcontours as your requirement

made the result as shown below.

Code:- Here is the C++ code for the above steps,

Mat tmp,thr;
Mat src=imread("river.jpg",1); //Your processed  image

cvtColor(src,tmp,CV_BGR2GRAY);
threshold(tmp,thr,10,255,THRESH_BINARY_INV);

    //Find biggest contour to segment exact object
    vector< vector <Point> > contours; // Vector for storing contour
    vector< Vec4i > hierarchy;
    int largest_contour_index=0;
    int largest_area=0;
    Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0)); //create destination image

    findContours( thr, contours, hierarchy,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
     for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
      {
       double a=contourArea( contours[i],false);  //  Find the area of contour
       if(a>largest_area){
       largest_area=a;
       largest_contour_index=i;                //Store the index of largest contour
       }
      }

     drawContours( dst,contours, largest_contour_index, Scalar(255,255,255),CV_FILLED, 8, hierarchy ); // Draw the largest contour using previously stored index.

     ///Either directly draw the contour
     drawContours( src,contours, largest_contour_index, Scalar(0,255,0),3, 8, hierarchy ); 

     ///Or use convex hull to draw the contour
     vector<vector<Point> >hull(1);
     convexHull(contours[largest_contour_index],  hull[0],false,true );
     drawContours( src, hull, 0, Scalar(0,0,255), 2, 8, vector<Vec4i>(), 0, Point() );
     imshow("contour",dst);
     imshow("src",src);

Result:- Red boundary is result of convexhull and Green boundary is the result of drawcontour using largest contour.contour.