Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7130
Title: Segmentation of textured images based on fractals and image filtering
Authors: Charalampidis, Dimitrios
Georgiopoulos, Michael N.
Kasparis, Takis 
Keywords: Image processing
Image analysis
Pattern recognition
Filters and filtration
Issue Date: 2001
Publisher: Elsevier
Source: Pattern Recognition, 2001, Volume 34, Issue 10, Pages 1963-1973
Abstract: This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-filtering and fractal features. Traditionally, filter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not intensity information. To reduce the total number of features used in the segmentation, the significance of each feature is examined using a test similar to the F-test, and less significant features are not used in the clustering process. The commonly used K-means algorithm is extended to an iterative K-means by using a variable window size that preserves boundary details. The number of clusters is estimated using an improved hierarchical approach that ignores information extracted around region boundaries.
URI: http://ktisis.cut.ac.cy/handle/10488/7130
ISSN: 00313203
DOI: 10.1016/S0031-3203(00)00126-6
Rights: © 2001 Pattern Recognition Society
Appears in Collections:Άρθρα/Articles

Show full item record

SCOPUSTM   
Citations 5

40
checked on May 16, 2017

WEB OF SCIENCETM
Citations 5

32
checked on May 7, 2017

Page view(s)

7
Last Week
0
Last month
2
checked on Jun 24, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.