DocumentCode
1425898
Title
Similarity and symmetry measures for convex shapes using Minkowski addition
Author
Heijmans, Henk J A M ; Tuzikov, Alexander V.
Author_Institution
CWI, Amsterdam, Netherlands
Volume
20
Issue
9
fYear
1998
fDate
9/1/1998 12:00:00 AM
Firstpage
980
Lastpage
993
Abstract
This paper is devoted to similarity and symmetry measures for convex shapes whose definition is based on Minkowski addition and the Brunn-Minkowski inequality. This means, in particular, that these measures are region-based, in contrast to most of the literature, where one considers contour-based measures. All measures considered in this paper are invariant under translations; furthermore, they can be chosen to be invariant under rotations, multiplications, reflections, or the class of affine transformations. It is shown that the mixed volume of a convex polygon and a rotation of another convex polygon over an angle θ is a piecewise concave function of θ. This and other results of a similar nature form the basis for the development of efficient algorithms for the computation of the given measures. Various results obtained in this paper are illustrated by experimental data. Although the paper deals exclusively with the two-dimensional case, many of the theoretical results carry over almost directly to higher-dimensional spaces
Keywords
computational complexity; image processing; symmetry; Brunn-Minkowski inequality; Minkowski addition; convex polygon; convex shapes; efficient algorithms; mixed volume; multiplication invariance; piecewise concave function; reflection invariance; region-based measures; rotation invariance; similarity measures; symmetry measures; transformation invariance; translation invariance; Cameras; Distortion measurement; Helium; Humans; Machine vision; Particle measurements; Reflection; Reflectivity; Rotation measurement; Shape measurement;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.713363
Filename
713363
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