A symmetric convexity measure

A new area-based convexity measure for polygons is described. It has the desirable properties that it is not sensitive to small boundary defects, and it is more symmetric with respect to intrusions and protrusions than other published convexity measures. The measure requires a maximally overlapping...

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Bibliographic Details
Published in:Computer vision and image understanding 2006-08, Vol.103 (2), p.101-111
Main Authors: Rosin, Paul L., Mumford, Christine L.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Convex hull
Convex skull
Convexity
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Polygon
Shape measure
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Summary:A new area-based convexity measure for polygons is described. It has the desirable properties that it is not sensitive to small boundary defects, and it is more symmetric with respect to intrusions and protrusions than other published convexity measures. The measure requires a maximally overlapping convex polygon, and this is efficiently estimated using a genetic algorithm (GA1). A second genetic algorithm (GA2) is then used to fine tune the result. In addition, the convex polygon is used to generate other values, measuring the amount of protrusions and intrusions that a polygon contains. Furthermore, the scheme can be modified to find the convex skull, which yields another new convexity measure. Examples of the measures’ application to medical image analysis are shown.
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2006.04.002