Abstract
This thesis introduces a multi-feature based texture segmentation and classifi cation algorithm. We first present general background on segmentation approaches which can be categorized into three groups : characteristic thresholding and clus tering, edge detection, and region merging. According to our texture segmentation objective, we choose characteristic thresholding and clustering along with region merging as our segmentation method which is accomplished by a popular K-mean algorithm applied in the feature space. Fractal geometry is receiving increasing attention as a model for natural phe nomena. We present an efficient method for estimating the fractal dimension of image surfaces and show that it performs better than other approaches used in seg mentation algorithms in the past. Since the fractal dimension alone is not sufficient to characterize natural texture, we use also texture energy measures and combine them with fractal dimension as a feature pair to describe and segment natural tex ture images. Finally, classification follows the segmentation result. Experimental results are also presented. To simulate enviromental effects, we also add noise to the original image and examine the noise endurance of these fea tures. It is found that the fractal dimension and texture energy feature pair performs well in characterizing natural texture; segmentation and classification results are also satisfactory even in noisy enviroments.
Notes
This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by STARS for more information.
Graduation Date
1991
Semester
Fall
Advisor
Kasparis, Takis
Degree
Master of Science (M.S.)
College
College of Engineering
Department
Electrical Engineering
Degree Program
Electrical Engineering
Format
Pages
134 p.
Language
English
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Identifier
DP0027991
Subjects
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
STARS Citation
Chen, Qing, "Texture segmentation and classification using fractal and energy measures" (1991). Retrospective Theses and Dissertations. 3811.
https://stars.library.ucf.edu/rtd/3811
Accessibility Status
PDF accessibility verified using Adobe Acrobat Pro Accessibility Checker