Paper
4 January 1995 Multicolor well-composed pictures
Author Affiliations +
Proceedings Volume 2356, Vision Geometry III; (1995) https://doi.org/10.1117/12.198624
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States
Abstract
As was noted early in the history of computer vision, using the same adjacency relation for the entire digital picture leads to so-called `paradoxes' related to the Jordan Curve Theorem. The most popular idea to avoid these paradoxes in binary images was using different adjacency relations for the foreground and the background: 8-adjacency for black points and 4-adjacency for white points, or vice versa. This idea cannot be extended in a straightforward way to multicolor pictures. In this paper a solution is presented which guarantees avoidance of the connectivity paradoxes related to the Jordan Curve Theorem for all multicolor pictures. Only one connectedness relation is used for the entire digital picture, i.e., for every component of every color. The idea is not to allow a certain `critical configuration' which can be detected locally to occur in digital pictures; such pictures are called `well-composed.' Well-composed pictures have very nice topological properties. For example, the Jordan Curve Theorem holds and the Euler characteristic is locally computable. This implies that properties of algorithms used in computer vision can be stated and proved in a clear way, and that the algorithms themselves become simpler and faster. Moreover, if a digitization process is guaranteed to preserve topology, then the obtained digital pictures must be well-composed.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Longin Jan Latecki "Multicolor well-composed pictures", Proc. SPIE 2356, Vision Geometry III, (4 January 1995); https://doi.org/10.1117/12.198624
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KEYWORDS
Binary data

Image processing

Vision geometry

Computer vision technology

Machine vision

Image segmentation

Process modeling

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