DocumentCode
949516
Title
Border and SurfaceTracing - Theoretical Foundations
Author
Brimkov, Valentin E. ; Klette, Reinhard
Author_Institution
Buffalo State Coll., Buffalo
Volume
30
Issue
4
fYear
2008
fDate
4/1/2008 12:00:00 AM
Firstpage
577
Lastpage
590
Abstract
In this paper, we define and study digital manifolds of arbitrary dimension, and provide (in particular) a general theoretical basis for curve or surface tracing in picture analysis. The studies involve properties such as the one-dimensionality of digital curves and (n - 1)-dimensionality of digital hypersurfaces that makes them discrete analogs of corresponding notions in continuous topology. The presented approach is fully based on the concept of adjacency relation and complements the concept of dimension, as common in combinatorial topology. This work appears to be the first one on digital manifolds based on a graph-theoretical definition of dimension. In particular, in the n-dimensional digital space, a digital curve is a one-dimensional object and a digital hypersurface is an (n - 1)-dimensional object, as it is in the case of curves and hypersurfaces in the Euclidean space. Relying on the obtained properties of digital hypersurfaces, we propose a uniform approach for studying good pairs defined by separations and obtain a classification of good pairs in arbitrary dimension. We also discuss possible applications of the presented definitions and results.
Keywords
computational geometry; curve fitting; graph theory; surface fitting; (n - 1)-dimensionality; Euclidean space; combinatorial topology; continuous topology; curve tracing; digital curves; digital hypersurfaces; picture analysis; surface tracing; digital curve; digital geometry; digital hypersurface; digital manifold; digital topology; discrete dimension; good pair; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2007.70725
Filename
4359340
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