Paper Info
Title
Generation of Brightness Membership Functions for Labeled Image Segmentation
Abstract
A common problem in segmentation of monochrome images occurs when an image has a background of varying gray level such as gradually changing shades, or when collections we would like to call regions, or classes, assume some broad range of gray scales. These problems hinder the use of brightness feature within segmentation algorithms of monochrome images. In this paper we propose a method for deriving membership functions for the labels related to the Brightness feature, to be included within fuzzy labeled segmentation algorithms. With the aim to be useful for a wide range of detection and segmentation applications, the method is based on the use of a rule base analysis of the brightness histogram, heuristics, and probability to possibility transformations. We illustrate the suitability and applicability of our membership functions' generation method with applications to real data sets.
Year
DOI
Venue
2001
10.1109/FUZZ.2001.1009048
FUZZ-IEEE
Keywords
Field
DocType
brightness,fuzzy set theory,image segmentation,possibility theory,probability,brightness histogram,brightness membership functions,fuzzy labeled segmentation algorithms,heuristics,labeled image segmentation,membership functions,monochrome images,possibility,probability,rule base analysis,shades,varying gray level
Histogram,Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Fuzzy set,Artificial intelligence,Computer vision,Pattern recognition,Segmentation,Monochrome,Brightness,Machine learning
Conference
Volume
Citations 
PageRank 
2
1
0.40
References 
Authors
1
2
Name
Order
Citations
PageRank
Pilar Sobrevilla128541.28
Eduard Montseny227240.75