Image segmentation
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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.
Image segmentation
- 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.
- 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:
- 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