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Proceedings Paper

Generalized skeleton representation and adaptive rectangular decomposition of binary images
Author(s): Kenji Shoji
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Paper Abstract

This report presents a technique of decomposing an arbitrary binary image into the union of rectangles so that the number of rectangles becomes as small as possible. This decomposition is referred as adaptive rectangular decomposition. Decomposing a binary image into objects with a same basic shape but with different sizes is familiar as morphological skeleton decomposition. To implement adaptive rectangular decomposition, we generalize the discrete version of morphological skeleton decomposition by replacing a sequences of disks {nB}, n equals 0,1, (DOT)(DOT)(DOT) with a structuring element sequence {Bn}, where Bn equals Bn-1 (direct sum) Gn-1 and Gn is called a generator. A good selection of each generator in a generator sequence {Gn} makes a compact representation of a given binary image. In adaptive rectangular decomposition, we restrict each generator Gn by one of only two objects; the vertical 2-pixel line V and the horizontal 2-pixel line H. The adaptive rectangular decomposition algorithm selects the best sequence {Gn} using dynamic programming (DP) technique. In some experiments, we compared adaptive rectangular decomposition with other types of decomposition in the viewpoint of the time cost of morphological operations by decomposed structuring elements (decomposed binary images). Experimental results show that the time cost of the operations by the structuring elements represented by adaptive rectangular decompositions is smaller than the case of other types of decompositions.

Paper Details

Date Published: 1 June 1992
PDF: 12 pages
Proc. SPIE 1769, Image Algebra and Morphological Image Processing III, (1 June 1992); doi: 10.1117/12.60660
Show Author Affiliations
Kenji Shoji, Utsunomiya Univ. (Japan)

Published in SPIE Proceedings Vol. 1769:
Image Algebra and Morphological Image Processing III
Paul D. Gader; Edward R. Dougherty; Jean C. Serra, Editor(s)

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