@ARTICLE { Rsds153,
author = {Sinha, Divyendu and Laplante, Phillip}, 
title = {A rough set-based approach to handling spatial uncertainty in binary images}, 
journal = {Engineering Applications of Artificial Intelligence}, 
number = 	{(no. 1)}, 
volume = 17, 
pages = {97-110}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {February}, 
year = 2004, 
abstract = {In this paper we consider the problem of detecting binary objects using rough sets. We present a method for constructing a gray-scaled (or, fuzzy) template for use in correlation-based matching of Boolean images. We assume a cause for spatial uncertainty that is quite common in machine vision applications and present a methodology for modeling it indirectly in the construction of the template. Our technique is computationally efficient and is superior to correlation-based techniques, which can be easily fooled and automates the hand-selection of structuring elements for the hit-or-miss transform technique, both of which are usually used to solve this problem.}, 
keywords = {rough sets, spatial uncertainty, pattern-matching, }, 
}