Paper Info
Title
Alleviation Of An Indeterminacy Problem Affecting Two Classical Iterative Image Thresholding Algorithms
Abstract
Thresholding algorithms are being increasingly used in a wide variety of disciplines to objectively discern patterns and objects in micrographs, still pictures or remotely-sensed images. Our experience has shown that three common thresholding algorithms exhibit indeterminacy, in that different operator inputs may lead to very different pattern characterizations. A grayscale image of a soil profile is used to illustrate this phenomemon in the case of the intermeans (IM), minimum error (ME), and Besag's iterated conditional modes (ICM) algorithms. For the illustrative example, the IM algorithm depends only weakly on the starting point of the iterative process - it converges to only two adjacent threshold values. In contrast, the ME algorithm converges to 14 different threshold values plus a segmentation that identifies the entire image as dye, and one that identifies none of it as dye. The ICM algorithm converges to an even wider variety of final segmentations, depending on its starting point. A noniterative modification of the IM and ME algorithms is proposed, providing a consistent method for choosing from among a set of apparently equally-valid segmentations.
Year
DOI
Venue
2006
10.1142/S021800140600448X
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
segmentation, thresholding, minimum error, intermeans, iterated conditional modes
Iterative and incremental development,Pattern recognition,Segmentation,Algorithm,Artificial intelligence,Operator (computer programming),Thresholding,Iterated conditional modes,Grayscale,Machine learning,Mathematics,Indeterminacy problem
Journal
Volume
Issue
ISSN
20
1
0218-0014
Citations 
PageRank 
References 
1
0.38
4
Authors
2
Name
Order
Citations
PageRank
CHARLES W. BOAST110.38
P. C. Baveye272.54