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May 2, 2023

A Study and Comparison of Different Image Segmentation Algorithms

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Preamble

Project Title : Detection of counterfeit Indian currency note Seminar Title : A Study and Comparison of Different Image

Segmentation Algorithms In our project we are dividing an image into 3x3 grid and

extract the required features and compare it with the database. The paper has given vivid description about the different segmentation algorithms, which are used in the applications like pattern recognition and image analysis.

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Agenda

Introduction

Image segmentation techniques

Results and discussion

Conclusion

References

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Section-1 : INTRODUCTION

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Continued..

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Section-2:Image Segmentation Techniques

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Thresholding base Image Segmentation

Image segmentation based on a thresholding is the simplest technique

In this technique we set a threshold value (mostly from the histogram of the image)

Pixel lying above or below can be classify as object and background

This technique convert a gray scale image into binary image

This technique will give good result if background and object has large variation in their intensity value

The disadvantage is that it will not be able to identify multiple object.

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Image segmentation algorithms can be classified into two classes:

• Global segmentation algorithms

• Local segmentation algorithms

Algorithms:

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Region-base Image Segmentation

Region based segmentation can be done in two ways: Region Growing Data Clustering

1. Region Growing: Region growing is simplest in region base image segmentation techniques. In this technique, a seed point is chosen at random, then neighboring pixels are check, with some criterion, to determine whether those neighboring pixels are added to the initial seed points or not.

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2. Data clustering: Data clustering method initially assume whole image as a single cluster and then use mathematics and statistics to create number of clusters within the image.Two types of clustering are possible: Hierarchical clustering Partitional clustering

In the hierarchical clustering, we can change the numbers of cluster during the process.

In the partitional clustering, we must decide the numbers of cluster before processing.

Continued…

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Edge based segmentation techniques are first find the edges by using different-different operators.

Since an object can be represented by its edges. So we can segment the image by simply finding edges in the image. A typical approach to segmentation using edges is:

• compute an edge image from original image• process the edge image for broken edges• transform the result to an ordinary segmented image

by filling in the object boundaries watershed segmentation technique is one more technique

which can be used to process the edge image.

Edge base Image Segmentation

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Section-3: Results and Comparison

Original Images

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Result obtained by Otsu's Algorithm

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Result obtained by K-Mean Clustering technique

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Result obtained by quad tree technique

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Result obtained by delta E technique

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Result obtained by FTH technique

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Used and compared the performance 6 image segmentation algorithms.

We have apply these algorithm on a very simple image (bear) to a very complex image (man woman).

From the simulation results, we can conclude that if test image is simple (one object) than Delta-E perform better as compare to other algorithm.

Although Otsu's and Kmean algorithm perform similar to Delta-E but they falsely consider background as a object.

As the Complexity of input test image increases, for single object segmentation, performance degraded. As we can see from tiger food image. for Complex image, if we run the same code for number of objects, than we might get the good performance and this would be a future work.

Conclusion

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Questions??

Stay Hungry Stay Foolish “Steve Jobs”

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