Paper Info
Title
Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation
Abstract
Images are by nature fuzzy. Approaches to object information extraction from images should attempt to use this fact and retain fuzziness as realistically as possible. In past image segmentation research, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n -dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples all drawn from medical imaging.
Year
DOI
Venue
1996
10.1006/gmip.1996.0021
CVGIP: Graphical Model and Image Processing
Keywords
Field
DocType
object definition,fuzzy connectedness,image segmentation,information extraction
Data structure,Fuzzy classification,Fuzzy set operations,Segmentation,Fuzzy logic,Algorithm,Fuzzy set,Image segmentation,Information extraction,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
58
3
Graphical Models and Image Processing
Citations 
PageRank 
References 
335
28.16
13
Authors
2
Search Limit
100335
Name
Order
Citations
PageRank
Jayaram K. Udupa12481322.29
Supun Samarasekera279285.72