This document summarizes techniques for image segmentation based on global thresholding and gradient-based edge detection. It discusses image segmentation, approaches like thresholding and edge detection in MATLAB. Thresholding is demonstrated on sample images to extract objects at different threshold values. Edge detection is also shown using Sobel filters. Issues like segmenting similar objects and boundary detection in the presence of noise are mentioned.

1. Central Institute Of Technology , Kokrajhar
IMAGE SEGMENTATION
Based on
Global Thresholding &
Gradient based Edge detection
Presented By:
Roshan Adhikari ( Gau-c-12/86)
Tubur Borgoyary (Gau-c-12/L-187)
Under the supervision of
Dr. Pankaj Pratap Singh
Asst. Prof.

2. CONTENTS:
Introduction
Image Segmentation
Identified issues in image segmentation
Analysis of image segmentation approach
Segmented algorithm
Thresholding based segmentation
Edge detection method
Line Detection
Global and Local thresholding

3. INTRODUCTION:
In the current scenario, Images have the lots of information
and the major challenge is to segment the relevant
information from the images.
It can be possible by applying the segmentation approaches
in effective manner.

4. WHAT IS IMAGE SEGMENTATION?
IMAGE SEGMENTATION IS THE PROCESS OF PARTITIONING A DIGITAL IMAGE INTO
MULTIPLE SEGMENTS . THE GOAL OF SEGMENTATION IS TO SIMPLIFY AND/OR CHANGE
THE REPRESENTATION OF AN IMAGE INTO SOMETHING THAT IS MORE MEANINGFUL
AND EASIER TO ANALYZE . IMAGE SEGMENTATION IS TYPICALLY USED TO LOCATE
OBJECTS AND BOUNDARIES (LINES, CURVES, ETC.) IN IMAGES .

5. Techniques used for Image segmentation:
Thresholding
Edge Detection

6. Image Thresholding in Matlab:
>> i=imread(‘imagename.jpg’); //Read the image
>> x=rgb2gray(i); // convert RGB to GRAY scale image.
>> figure, imshow(x); //show the converted image
>> figure, imhist(x); //display histogram of the converted image
>> figure, imhist(x,64);
>> x1=histeq(x); // histogram equalization to enhance the contraction an image
>> figure, imshow(x1); //display the equalization image.
>> figure ,imhist(x1); //display histogram of the eqalisation image.
>> t=max(x1(:)); // maximum value for whole matrix
>> h=x1>=t; //Applying thresholding to image x1 to get logical image showing point.
>> imshow(h) // Display the image.
>> tmax=240; //Taking value of ‘t’ as 240
>> h=x1>=tmax;
>> imshow(h); //Show the image.

7. Original image:

8. >> i=imread(‘ima.png’); //Read the image
>> x=rgb2gray(i); // convert RGB to GRAY scale image.
>> figure, imshow(x) //show the converted image

9. >> figure, imhist(x) //display histogram of the converted image

10. >> x1=histeq(x); // histogram eqalization to enhance the contraction an image
>> figure, imshow(x1); //display the eqalization image

11. >> figure imhist(x1); //display histogram of the eqalisation image.

12. >> t=max(x1(:)); // maximum value for whole matrix
>> h=x1>=t; //Applying thresholding to image x1 to get logical image showing point.
>> imshow(h); // Display the image.

13. >> tmax=200; //Taking value of t as 200
>> h=x1>=tmax;
>> imshow(h); //Show the image
T=200 T=100

14. Edge detection method:
>>m-=edge(x,’sobel’);
>>Imshow(m);
Edge detection using sobel technique:

15. m1=edge(x1,’sobel’,’vertical’)

16. m1=edge(x,’sobel’,’horizontal’);

17. MAJOR PROBLEMS:
1.SEGMENTATION IN SIMILAR OBJECT:
Due to similar pixel behavior (or approximately ), there may be problem to
distinguish the objects.
2. Boundary detection :
Boundary detection problems occur due to noise (irrelevant pixels). Edge
detection is the common problem in segmentation and also important for
analyzing the boundaries in images. This technique is generally used for
finding the discontinuities in gray level images.

18. Data used:
The binary and greyscale image data will used in the
proposed techniques for image segmentation
Software used:
MATLAB

19. REFERENCE:
1. Digital IMAGE PROCESSING by GONZALEZ.

20. THANKS