Paper Info
Title
A fuzzy rule-based colour image segmentation algorithm
Abstract
Most fuzzy rule-based image segmentation techniques to date have been primarily developed for gray level images. In this paper, a new algorithm called fuzzy rule-based colour image segmentation (FRCIS) is proposed by extending the generic fuzzy rule-based image segmentation (GFFUS) algorithm G.C. Karmakar, L.S. Dooley [2002] and integrating a novel algorithm for averaging hue angles. Qualitative and quantitative analysis of the performance of FRCIS is examined and contrasted with the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms for both the hue-saturation-value (HSV) and RGB colour models. Overall, FRCIS provides considerable improvement for many different image types.
Year
DOI
Venue
2003
10.1109/ICIP.2003.1247128
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference
Keywords
Field
DocType
fuzzy logic,image colour analysis,image segmentation,RGB colour model,fuzzy c-means algorithm,fuzzy rule-based colour image segmentation,gray level image,hue angle averaging,hue-saturation-value,possibilistic c-means algorithm,red, green, blue colour
Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,RGB color model,Computer vision,Pattern recognition,Segmentation,Image texture,Fuzzy logic,Algorithm,Fuzzy rule
Conference
Volume
ISSN
ISBN
1
1522-4880
0-7803-7750-8
Citations 
PageRank 
References 
2
0.45
7
Authors
3
Name
Order
Citations
PageRank
Laurence Dooley1605.36
Gour C. Karmakar220.45
Manzur Murshed396980.94