Paper Info
Title
Comparison of fuzzy connectedness and graph cut segmentation algorithms
Abstract
The goal of this paper is a theoretical and experimental comparison of two popular image segmentation algorithms: fuzzy connectedness (FC) and graph cut (GC). On the theoretical side, our emphasis will be on describing a common framework in which both of these methods can be expressed. We will give a full analysis of the framework and describe precisely a place which each of the two methods occupies in it. Within the same framework, other region based segmentation methods, like watershed, can also be expressed. We will also discuss in detail the relationship between FC segmentations obtained via image forest transform (IFT) algorithms, as opposed to FC segmentations obtained by other standard versions of FC algorithms. We also present an experimental comparison of the performance of FC and GC algorithms. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as influence of the choice of the seeds on the output.
Year
DOI
Venue
2011
10.1117/12.872522
Proceedings of SPIE
Keywords
Field
DocType
graph cut,algorithms,image segmentation
Cut,Computer vision,Scale-space segmentation,Segmentation,Graph cut segmentation,Algorithm,Image segmentation,Watershed,Fuzzy connectedness,Artificial intelligence,Image segmentation algorithm,Physics
Conference
Volume
ISSN
Citations 
7962
0277-786X
12
PageRank 
References 
Authors
0.85
13
4
Name
Order
Citations
PageRank
Krzysztof Ciesielski129629.71
Jayaram K. Udupa22481322.29
Alexandre X. Falcão31877132.30
Paulo A. V. Vechiatto Miranda431326.26