From b1f5c3f8d6671b4331cef1dcebdf63b7a43a3a2b Mon Sep 17 00:00:00 2001
From: priyanka
Date: Wed, 24 Jun 2015 15:03:17 +0530
Subject: initial commit / add all books

---
 3176/CH10/EX10.9/Ex10_9.sce | 84 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 3176/CH10/EX10.9/Ex10_9.sce

(limited to '3176/CH10/EX10.9/Ex10_9.sce')

diff --git a/3176/CH10/EX10.9/Ex10_9.sce b/3176/CH10/EX10.9/Ex10_9.sce
new file mode 100644
index 000000000..2969facb7
--- /dev/null
+++ b/3176/CH10/EX10.9/Ex10_9.sce
@@ -0,0 +1,84 @@
+//Ex10_9
+// Another illustration of the three principal Edge Detection Methods
+// Version : Scilab 5.4.1
+// Operating System : Window-xp, Window-7
+//Toolbox: Image Processing Design 8.3.1-1
+//Toolbox: SIVP 0.5.3.1-2
+//Reference book name : Digital Image Processing
+//book author: Rafael C. Gonzalez and Richard E. Woods
+
+clc;
+close;
+clear;
+xdel(winsid())//to close all currently open figure(s).
+
+a=imread("Ex10_9.tif");
+//a=im2double(a);
+figure,ShowImage(a,'Gray Image');
+title('Original Image','color','blue','fontsize',4);
+[M,N]=size(a);
+ //////////////////  Threshlded Gradient of Smoothed Image  ////////////////////////////////
+a1=imfilter(a,fspecial('average',5));
+Mask=[-1 -2 -1;0 0 0;1 2 1];  // Mask for the Sobel
+GradientX_Image=imfilter(a1,Mask);  // Filtering the Original Image with the Mask
+//figure,ShowImage(GradientX_Image,'Filter Image');
+//title('Sobel X-direction Gradient Image','color','blue','fontsize',4);
+
+Mask=[-1 0 1;-2 0 2;-1 0 1];  // Mask for the Sobel
+GradientY_Image=imfilter(a1,Mask);  // Filtering the Original Image with the Mask
+//figure,ShowImage(GradientY_Image,'Filter Image');
+//title('Sobel Y-direction Gradient Image','color','blue','fontsize',4);
+
+Gradient=GradientX_Image+GradientY_Image;
+//figure,ShowImage(Gradient,'Filter Image');
+//title('Sobel X+Y Gradient Image','color','blue','fontsize',4);
+
+th=84;  // 33% of the Maximum Value in Gradient Image
+[row col]=find(Gradient>84);
+Gradient_Thresh=zeros(M,N);
+for i=1:length(row)
+    Gradient_Thresh(row(i),col(i))=255;
+end
+figure,ShowImage(Gradient_Thresh,'Filter Image');
+title('Thresholded gradient Image','color','blue','fontsize',4);
+
+//////////////////// Marr-Hildreth Edge Detection ///////////////////////////////////
+a=im2double(a);
+sigma=3;
+for i=1:19
+    for j=1:19
+        Mask(i,j)=[(i^2+j^2-(2*sigma^2))/sigma^4]*exp(-(i^2+j^2)/(2*sigma^2));  // Mask Generation
+    end
+end
+
+Filter_Image=imfilter(a,Mask);  // Filtering the Original Image with the Mask
+//figure,ShowImage(Filter_Image,'Filter Image');
+//title('Laplacian of gaussian Image','color','blue','fontsize',4);
+b=zeros(M,N);
+temp=Filter_Image;
+th=0.0021;
+for i=2:M-1  // Zero Crossing Detection
+    for j=2:N-1
+        //temp=[Filter_Image(i-1:i+1,j-1:j+1)];
+        if((temp(i-1,j-1)>th & temp(i+1,j+1)<th) | (temp(i-1,j-1)<th & temp(i+1,j+1)>th)) then
+            b(i,j)=255;
+        else if ((temp(i-1,j+1)>th & temp(i+1,j-1)<th) | (temp(i-1,j+1)<th & temp(i+1,j-1)>th)) then
+            b(i,j)=255;
+        else if ((temp(i,j+1)>th & temp(i,j-1)<th) | (temp(i,j+1)<th & temp(i,j-1)>th)) then
+            b(i,j)=255;
+        else if ((temp(i-1,j)>th & temp(i+1,j)<th) | (temp(i,j+1)<th & temp(i,j-1)>th)) then
+            b(i,j)=255;
+        end
+        end
+        end
+        end            
+    end
+end
+figure,ShowImage(b,'Zero Crossing Image');
+title('Marr-Hildreth Edge Detected Image','color','blue','fontsize',4);
+
+////////////////////////// Canny Edge Detecedd Image  //////////////////////////////////////
+a=imread("Ex10_9.tif");
+E=edge(a,'canny',[0.05 0.95]);
+figure,ShowImage(E,'Canny Image');
+title('Canny Edge Detected Image','color','blue','fontsize',4);
-- 
cgit