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Object shape before boundary shape: Scale-space medial axes
- Source :
- Journal of Mathematical Imaging and Vision. 4:303-313
- Publication Year :
- 1994
- Publisher :
- Springer Science and Business Media LLC, 1994.
-
Abstract
- Representing object shape in two or three dimensions has typically involved the description of the object boundary. This paper proposes a means for characterizing object structure and shape that avoids the need to find an explicit boundary. Rather, it operates directly from the imageintensity distribution in the object and its background, using operators that do indeed respond to “boundariness.” It produces a sort of medial-axis description that recognizes that both axis location and object width must be defined according to a tolerance proportional to the object width. This generalized axis is called themultiscale medial axis because it is defined as a curve or set of curves in scale space. It has all of the advantages of the traditional medial axis: representation of protrusions and indentations in the object, decomposition of object-curvature and object-width properties, identification of visually opposite points of the object, incorporation of size constancy and orientation independence, and association of boundary-shape properties with medial locations. It also has significant new advantages: it does not require a predetermination of exactly what locations are included in the object, it provides gross descriptions that are stable against image detail, and it can be used to identify subobjects and regions of boundary detail and to characterize their shape properties.
- Subjects :
- Statistics and Probability
Axis-aligned object
Applied Mathematics
Geometry
Object structure
Condensed Matter Physics
Scale space
Subjective constancy
Medial axis
Modeling and Simulation
sort
Geometry and Topology
Computer Vision and Pattern Recognition
Boundary shape
Shape analysis (digital geometry)
Mathematics
Subjects
Details
- ISSN :
- 15737683 and 09249907
- Volume :
- 4
- Database :
- OpenAIRE
- Journal :
- Journal of Mathematical Imaging and Vision
- Accession number :
- edsair.doi...........5839f40a98608bee91d2d8cdcff7554f