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150960

Coarse Segmentation of Textured Images Using Variance Analysis.

Article

Last updated: 22 Jan 2023

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Subjects

-

Tags

Computer Engineering and Systems

Abstract

This paper presents a novel approach for the segmentation of a textured scene. The algorithm is image-based; no specific model is assumed for the image. Also, no a priori knowledge about the different texture regions, neither their number, nor their behavior is assumed. The algorithm partitions the image into small disjoint square windows, and the Variance for each window data is calculated. Then the K-means clustering algorithm is applied upon these windows. Together with the K-means algorithm, a new distance measure has been defined. This new distance measure was deduced from a statistical test known as Bartlett's test based on the variance of the window's data. The same statistical test has also been applied but in a different fashion to determine the number of different textures in the image. The new image-based distance measure has been tested and compared to a model-based Euclidean distance measure, with each window modeled by a non-causal Gaussian Markov Random Field (GMRF). The results of the comparison have shown that the new distance measure is much simpler and faster, while yielding to a still robust and effective segmentation. 

DOI

10.21608/bfemu.2021.150960

Authors

First Name

Fatma

Last Name

Samir

MiddleName

-

Affiliation

National Telecommunication Institute., 5 Mokhaiam El-Dam Street., Naser City., Cairo., Egypt.

Email

-

City

Cairo

Orcid

-

First Name

Ayman

Last Name

Ibrahim

MiddleName

A.M.

Affiliation

National Telecommunication Institute.,5 Mokhaiam El-Dam., Cairo., Egypt.

Email

-

City

-

Orcid

-

First Name

Samir

Last Name

Shaheen

MiddleName

I.

Affiliation

Computer Engineering Department., Faculty of Engineering., Cairo University., Giza., Egypt.

Email

-

City

Giza

Orcid

-

Volume

22

Article Issue

3

Related Issue

22113

Issue Date

1997-09-01

Receive Date

1997-05-10

Publish Date

2021-09-01

Page Start

1

Page End

12

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 22_Issue 3_Pages 1-12.pdf

PDF . 161.0kB

Link

https://bfemu.journals.ekb.eg/article_150960.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=150960

Order

5

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023