Texture Segmentation Using Fractal Features

Abstract

In this work, we propose the application fractal compression techniques to textured images segmentation, and use the transformation coefficients as features for segmentation. The result is improved by combining fractal dimension feature and the transformation coefficients from the original and its filter versions. Feature vectors are clustered together using K-mean algorithm with features pre-smoothing. The numbers of feature are minimized to reach the compromise result. In the integrated approach, we attempt to improve segmentation of texture images using our method. Background knowledge of image segmentation and image compression will be presented. Algorithms for fractal dimension calculation, K-means clustering, and fractal compression is given. Experimental results are included, and possible future work is mentioned.

Notes

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Thesis Completion

2000

Semester

Spring

Advisor

Kasparis, Takis

Degree

Bachelor of Science (B.S.)

College

College of Engineering

Degree Program

Electrical Engineering

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Format

Print

Identifier

DP0021537

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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