diff --git a/8thWeek/Canny/Canny.pde b/8thWeek/Canny/Canny.pde
index 48aa885..f947ee4 100644
--- a/8thWeek/Canny/Canny.pde
+++ b/8thWeek/Canny/Canny.pde
@@ -1,168 +1,166 @@
 PImage img;
-PImage board1Thresholded;
+PImage board1Thresholded, board1Blurred, board1Scharr;
 HScrollbar thresholdBar0;
 HScrollbar thresholdBar1;
 
 void settings() {
   size(1600, 600);
 }
 
 void setup() {
   thresholdBar0 = new HScrollbar(0, 560, 800, 20);
   thresholdBar1 = new HScrollbar(0, 580, 800, 20);
   img = loadImage("src/board1.jpg");
   board1Thresholded = loadImage("src/board1Thresholded.bmp");
+  board1Blurred = loadImage("src/board1Blurred.bmp");
+  board1Scharr = loadImage("src/board1Scharr.bmp");
+
   noLoop(); // no interactive behaviour: draw() will be called only once.
 }
 
 void draw() {
   image(img, 0, 0);//show image
-  PImage img2 = img.copy();//make a deep copy
-  img2.loadPixels();// load pixels
+  PImage img2,img3,img4;
 
   //img2=threshold(img2, (int)map(thresholdBar.getPos(),0,1,0,255), false);
   //img2=HueMap(img2, thresholdBar0.getPos(), thresholdBar1.getPos());
-  img2=thresholdHSB(img2, 100, 200, 100, 255, 45, 100 );
-
-  img2.updatePixels();//update pixels
+  img2 = thresholdHSB(img, 100, 200, 100, 255, 45, 100 );
+  img3 = convolute(img);
+  img4 = scharr(img);
+  
   image(img2, img.width, 0);
   /*thresholdBar0.display();
    thresholdBar0.update();
    thresholdBar1.display();
    thresholdBar1.update();*/
-   
-   println(imagesEqual(img2,board1Thresholded));
+  println(imagesEqual(img2, board1Thresholded));
+  println(imagesEqual(img3, board1Blurred));
+  println(imagesEqual(img4, board1Scharr));
 }
 
 PImage threshold(PImage img, int threshold, boolean inverted) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i]=brightness(img.pixels[i])>threshold^inverted?color(255):color(0);
   }
   return result;
 }
 
 PImage thresholdHSB(PImage img, int minH, int maxH, int minS, int maxS, int minB, int maxB) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     if (hue(img.pixels[i])<minH||maxH<hue(img.pixels[i])||
       saturation(img.pixels[i])<minS||maxS<saturation(img.pixels[i])||
       brightness(img.pixels[i])<minB||maxB<brightness(img.pixels[i])) {
       result.pixels[i]=0;
     } else {
       result.pixels[i]=Integer.MAX_VALUE;
     }
   }
   return result;
 }
 
 
 boolean imagesEqual(PImage img1, PImage img2) {
   if (img1.width != img2.width || img1.height != img2.height)
     return false;
   for (int i = 0; i < img1.width*img1.height; i++)
     //assuming that all the three channels have the same value
     if (red(img1.pixels[i]) != red(img2.pixels[i]))
       return false;
   return true;
 }
 
 PImage scharr(PImage img) {
   float[][] vKernel = {
-            { 3, 0, -3 }, 
-            { 10, 0, -10 }, 
-            { 3, 0, -3 } };
+    { 3, 0, -3 }, 
+    { 10, 0, -10 }, 
+    { 3, 0, -3 } };
   float[][] hKernel = {
-            { 3, 10, 3 }, 
-            { 0, 0, 0 }, 
-            { -3, -10, -3 } };
+    { 3, 10, 3 }, 
+    { 0, 0, 0 }, 
+    { -3, -10, -3 } };
   PImage result = createImage(img.width, img.height, ALPHA);
+  
   // clear the image
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i] = color(0);
   }
   float max=0;
-  float normFactor = 1.f;
   float[] buffer = new float[img.width * img.height];
+  
   // *************************************
   // Implement here the double convolution
   int N = 3; //kernel size
-  for (int x = 0; x< img.width; ++x) {
+  for (int x = 0; x<img.width; ++x) {
     for (int y = 0; y<img.height; y++) { 
       float sum_h = 0, sum_v = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
           int row = ((row = y-N/2+i) < 0)? 0 : row;
           row = (row >= img.height)? img.height-1 : row;
           int column = ((column = x-N/2+i) < 0)? 0 : column;
           column = (column >= img.width)? img.width-1: column;
           float brightness = brightness(img.pixels[column + img.width *row]);
           sum_h += hKernel[i][j] * brightness;
           sum_v += vKernel[i][j] * brightness;
         }
       }
-      // - sum all these intensities and divide it by normFactor
-      // - set result.pixels[y * img.width + x] to this value
       float value = sqrt(pow(sum_h, 2) + pow(sum_v, 2));
       max = (value > max )? value : max;
-      buffer[x+y*img.width] = value / normFactor;
+      buffer[x+y*img.width] = value;
     }
   }
-  
+
   // *************************************
+  
   for (int y = 1; y < img.height - 1; y++) { // Skip top and bottom edges
     for (int x = 1; x < img.width - 1; x++) { // Skip left and right
       int val=(int) ((buffer[y * img.width + x] / max)*255);
       result.pixels[y * img.width + x]=color(val);
     }
   }
   return result;
 }
 PImage convolute(PImage img) {
   int N = 3; //kernel size
   float[][] kernel1 = {{ 0, 0, 0 }, 
     { 0, 2, 0 }, 
     { 0, 0, 0 }};
   float[][] kernel2 = {{ 0, 1, 0 }, 
     { 1, 0, 1 }, 
     { 0, 1, 0 }};
   float[][] gaussianKernel = 
     {{ 9, 12, 9 }, 
     { 12, 15, 12 }, 
     { 9, 12, 9 }};
   float[][] kernel = gaussianKernel;
   float normFactor = 1.f;
-  /* For the gaussian kernel
-   for(int i = 0; i< N; ++i){
-   for(int j = 0; j<N; ++j){
-   normFactor += kernel[i][j];
-   }
-   }
-   */
+  for (int i = 0; i< N; ++i) {
+    for (int j = 0; j<N; ++j) {
+      normFactor += kernel[i][j];
+    }
+  }
+
   // create a greyscale image (type: ALPHA) for output
   PImage result = createImage(img.width, img.height, ALPHA);
   //
   // for each (x,y) pixel in the image:
   for (int x = 0; x< img.width; ++x) {
     for (int y = 0; y<img.height; y++) { 
-      // - multiply intensities for pixels in the range 
-      //(x - N/2, y - N/2) to (x + N/2, y + N/2) by the
-      //corresponding weights in the kernel matrix
       int sum = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
           int row = ((row = y-N/2+i) < 0)? 0 : row;
           row = (row >= img.height)? img.height-1 : row;
           int column = ((column = x-N/2+i) < 0)? 0 : column;
           column = (column >= img.width)? img.width-1: column;
           sum += kernel[i][j] * brightness(img.pixels[column + img.width *row]);
         }
       }
-      // - sum all these intensities and divide it by normFactor
-      // - set result.pixels[y * img.width + x] to this value
       result.pixels[x+y*img.width] = color((int)(sum / normFactor));
     }
   }
 
   return result;
 }
diff --git a/8thWeek/Canny/Canny.pde b/8thWeek/Canny/Canny5045667169087710313.autosave
similarity index 83%
copy from 8thWeek/Canny/Canny.pde
copy to 8thWeek/Canny/Canny5045667169087710313.autosave
index 48aa885..19df0e6 100644
--- a/8thWeek/Canny/Canny.pde
+++ b/8thWeek/Canny/Canny5045667169087710313.autosave
@@ -1,168 +1,169 @@
 PImage img;
-PImage board1Thresholded;
+PImage board1Thresholded,board1Scharr,board1Blurred;
 HScrollbar thresholdBar0;
 HScrollbar thresholdBar1;
 
 void settings() {
   size(1600, 600);
 }
 
 void setup() {
   thresholdBar0 = new HScrollbar(0, 560, 800, 20);
   thresholdBar1 = new HScrollbar(0, 580, 800, 20);
   img = loadImage("src/board1.jpg");
   board1Thresholded = loadImage("src/board1Thresholded.bmp");
+  board1Scharr = loadImage("src/board1Scharr.bmp");
+  board1Blurred = loadImage("src/board1Blurred.bmp");
   noLoop(); // no interactive behaviour: draw() will be called only once.
 }
 
 void draw() {
   image(img, 0, 0);//show image
-  PImage img2 = img.copy();//make a deep copy
-  img2.loadPixels();// load pixels
+  PImage img2 ,img3 ,img4;//make a deep copy
 
   //img2=threshold(img2, (int)map(thresholdBar.getPos(),0,1,0,255), false);
   //img2=HueMap(img2, thresholdBar0.getPos(), thresholdBar1.getPos());
-  img2=thresholdHSB(img2, 100, 200, 100, 255, 45, 100 );
-
-  img2.updatePixels();//update pixels
-  image(img2, img.width, 0);
+  img2=thresholdHSB(img, 100, 200, 100, 255, 45, 100 );
+  img3 = convolute(img);
+  img4 = scharr(img);
+  image(img4, img.width, 0);
   /*thresholdBar0.display();
    thresholdBar0.update();
    thresholdBar1.display();
    thresholdBar1.update();*/
    
+   
    println(imagesEqual(img2,board1Thresholded));
+   println(imagesEqual(img3,board1Blurred));
+   println(imagesEqual(img4,board1Scharr));
 }
 
 PImage threshold(PImage img, int threshold, boolean inverted) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i]=brightness(img.pixels[i])>threshold^inverted?color(255):color(0);
   }
   return result;
 }
 
 PImage thresholdHSB(PImage img, int minH, int maxH, int minS, int maxS, int minB, int maxB) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     if (hue(img.pixels[i])<minH||maxH<hue(img.pixels[i])||
       saturation(img.pixels[i])<minS||maxS<saturation(img.pixels[i])||
       brightness(img.pixels[i])<minB||maxB<brightness(img.pixels[i])) {
       result.pixels[i]=0;
     } else {
       result.pixels[i]=Integer.MAX_VALUE;
     }
   }
   return result;
 }
 
 
 boolean imagesEqual(PImage img1, PImage img2) {
   if (img1.width != img2.width || img1.height != img2.height)
     return false;
   for (int i = 0; i < img1.width*img1.height; i++)
     //assuming that all the three channels have the same value
-    if (red(img1.pixels[i]) != red(img2.pixels[i]))
+    if (color(img1.pixels[i]) != color(img2.pixels[i])){
+      println(i%img.width, i/img.width);
       return false;
+    }
   return true;
 }
 
 PImage scharr(PImage img) {
   float[][] vKernel = {
             { 3, 0, -3 }, 
             { 10, 0, -10 }, 
             { 3, 0, -3 } };
   float[][] hKernel = {
             { 3, 10, 3 }, 
             { 0, 0, 0 }, 
             { -3, -10, -3 } };
   PImage result = createImage(img.width, img.height, ALPHA);
   // clear the image
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i] = color(0);
   }
   float max=0;
-  float normFactor = 1.f;
   float[] buffer = new float[img.width * img.height];
   // *************************************
   // Implement here the double convolution
   int N = 3; //kernel size
-  for (int x = 0; x< img.width; ++x) {
-    for (int y = 0; y<img.height; y++) { 
+  for (int x = 1; x< img.width-1; ++x) {
+    for (int y = 1; y<img.height-1; y++) { 
       float sum_h = 0, sum_v = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
-          int row = ((row = y-N/2+i) < 0)? 0 : row;
-          row = (row >= img.height)? img.height-1 : row;
-          int column = ((column = x-N/2+i) < 0)? 0 : column;
-          column = (column >= img.width)? img.width-1: column;
-          float brightness = brightness(img.pixels[column + img.width *row]);
+          float brightness = brightness(img.pixels[x-N/2+i + img.width *(y-N/2+i)]);
           sum_h += hKernel[i][j] * brightness;
           sum_v += vKernel[i][j] * brightness;
         }
       }
       // - sum all these intensities and divide it by normFactor
       // - set result.pixels[y * img.width + x] to this value
       float value = sqrt(pow(sum_h, 2) + pow(sum_v, 2));
       max = (value > max )? value : max;
-      buffer[x+y*img.width] = value / normFactor;
+      buffer[x+y*img.width] = value;
     }
   }
   
   // *************************************
   for (int y = 1; y < img.height - 1; y++) { // Skip top and bottom edges
     for (int x = 1; x < img.width - 1; x++) { // Skip left and right
       int val=(int) ((buffer[y * img.width + x] / max)*255);
       result.pixels[y * img.width + x]=color(val);
     }
   }
   return result;
 }
 PImage convolute(PImage img) {
   int N = 3; //kernel size
   float[][] kernel1 = {{ 0, 0, 0 }, 
     { 0, 2, 0 }, 
     { 0, 0, 0 }};
   float[][] kernel2 = {{ 0, 1, 0 }, 
     { 1, 0, 1 }, 
     { 0, 1, 0 }};
   float[][] gaussianKernel = 
     {{ 9, 12, 9 }, 
     { 12, 15, 12 }, 
     { 9, 12, 9 }};
   float[][] kernel = gaussianKernel;
   float normFactor = 1.f;
   /* For the gaussian kernel
    for(int i = 0; i< N; ++i){
    for(int j = 0; j<N; ++j){
    normFactor += kernel[i][j];
    }
    }
    */
   // create a greyscale image (type: ALPHA) for output
   PImage result = createImage(img.width, img.height, ALPHA);
   //
   // for each (x,y) pixel in the image:
   for (int x = 0; x< img.width; ++x) {
     for (int y = 0; y<img.height; y++) { 
       // - multiply intensities for pixels in the range 
       //(x - N/2, y - N/2) to (x + N/2, y + N/2) by the
       //corresponding weights in the kernel matrix
       int sum = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
           int row = ((row = y-N/2+i) < 0)? 0 : row;
           row = (row >= img.height)? img.height-1 : row;
           int column = ((column = x-N/2+i) < 0)? 0 : column;
           column = (column >= img.width)? img.width-1: column;
           sum += kernel[i][j] * brightness(img.pixels[column + img.width *row]);
         }
       }
       // - sum all these intensities and divide it by normFactor
       // - set result.pixels[y * img.width + x] to this value
       result.pixels[x+y*img.width] = color((int)(sum / normFactor));
     }
   }
 
   return result;
 }