This document summarizes edge detection using the Sobel operator. It begins by defining edges as areas of significant intensity change between objects in an image. There are three main steps to edge detection: image smoothing to remove noise; edge point detection to identify areas of intensity change; and edge localization to precisely locate the edges. The document then explains how the Sobel operator uses two 3x3 kernels to detect horizontal and vertical edges by approximating the image gradient. It provides code to demonstrate Sobel edge detection in MATLAB. The advantages of the Sobel operator are its simplicity and ability to detect edges and their orientations.

1. EDGE DETECTION USING SOBEL
OPERATOR
SUBMITTED BY
Mohamed sheik mohideen S.E.A
Mohamed shiyam J

2. INTRODUCTION
 What is edge detection? This question has to be answered before someone goes deeper into the
topic for understanding.
 Edges are defined as "Sudden and Significant changes in the intensity" of an image.
 These changes happen between the boundaries of object in an image. One could refer below the
image.
 The Mug is seen in the input image and once edges are detected, one can find out the exact layout
or boundary of the object.
 The edges are detected based on the significant change in intensity between the objects in the
image.
 To make it precise, the mug has a different intensity from the grey background and it is the key
idea for us to identify the edges. So, in an image, if there are many objects, edges are the easiest
way to identify all of them

3. INTRODUCTION

4.  There should be sensible reasons for detecting the edges.
Yes, we do have many reasons for the same and we can
see one them one after another.
 One can understand the shape of objects in the image
only when the edges are detected. So, ideally to
understand an object and its shape, it becomes inevitable
for someone to detect the edges.
 There are many technical issues and challenges mapped
to the segmentation, registration and object identification
techniques.
 Edges prove to be efficient with these above techniques
at the fundamental levels.
Why to detect edges?

5. Any process has to follow a sequence and edge detection is no
different. There are three steps followed in Edge Detection process
They are:
 Image Smoothening
 Edge Points Detection and
 Edge Localization
Steps in Edge Detection

6.  Image Smoothening is all about removal of the noise from the image. In
addition, the removal or suppression of the noise should be done in such a
way that, the quality of the image is not altered.
 Essentially, image smoothening is all about making the image noise free. One
can refer to Fig to understand the process.
Image Smoothening

7. Edges are detected through identifying the
sudden changes in the intensity. Even noise is
all about sudden change in the intensity. Edge
Point Detections is a process where the noise
alone is carefully removed or discarded
retaining the edges appropriately.
Edge points detection

8. Edge localization
The final step in the sequence is edge
localization. Sometimes, the processes like
thinning, linking etc. are to be carried out
to locate the edges appropriately. This
process is called edge localization

9.  Sobel filter has two kernels (3 x 3 Matrix).
 One of them correspond the x (horizontal) and the other shall be used for the y (vertical)
direction.
 These two kernels shall be convoluted with the original image under process and through
which the edge points are calculated with ease.
 The kernel values shown below are fixed for sobel filter and cannot be altered.
 The Gaussian filter play a vital role in the entire process.The fundamental idea behind
Gaussian filter is the centre having more weight than the rest.The general approach for
detecting the edges is with the first order or second order
Sobel filter

10. BLOCK DIAGRAM

11. SOURCE CODE
%Sobel filter
close all;clearall;clc;
img=imread(‘D:lena .jpg);
imshow(img);
title(‘original image’)
figure;
g_1=rgb2gray(img);
imshow(g_1);
title(‘Gray level image’);
figure;
s=fspecial(‘sobel’);
sobel=uint8(round(filter2(s,g_1)));
imshow(sobel);
title(‘sobel filter’)

12. OUTPUT

13. Application
 The Sobel method, or Sobel filter, is a gradient-based method
that looks for strong changes in the first derivative of an
image.
 The Sobel edge detector uses a pair of 3 x 3 convolution
masks, one estimating the gradient in the x- direction and the
other in the y-direction.

14.  The primary advantages of the Sobel operator lie in its simplicity.
The Sobel method provides a approximation to the gradient
magnitude.
 Another advantage of the Sobel operator is it can detect edges and
their orientations.
Advantages

15. CONCLUSION
 Thus the project with function using Matlab and It was executed successfully
 It is the basic idea of image processing using Sobel filter
 In later period of time we add an additional feature As a image processing in
Sobel filter.

16. THANK YOU