APPENDICES and SUPPLEMENTS
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A Corrigenda
for this article was issued by the authors for this article in Communications of the ACM volume 27, issue 2 (February 1984), page 151.
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ABSTRACT
As printed
Quadtree skeletons are exact representations of the image and
are used because they are observed to yield space efficiently and a
decreased sensitivity to shifts in contrast with the quadtree. The
QMAT can be used as the underlying representation when solving most
problems that can be solved by using a quadtree. An algorithm is
presented for the computation of the QMAT of a given quadtree by
only examining each BLACK node's adjacent and abutting
neighbors.
Corrected Abstract (published as corrigendum in CACM 27, 2
(February 1984) p. 151)
The skeletal and medial axis transform concepts used in
traditional image processing representations are adapted to the
quadtree representation. The result is the definition of of a new
data structure termed the Quadtree Medial Axis Transform (QMAT). A
QMAT results in a partition of the image into a set of nondisjoint
squares having sides whose lengths are sums of powers of 2 rather
than, as is the case with quadtrees, a set of disjoint squares
having sides of lengths which are powers of 2. The motivation is
not to study skeletons for the usual purpose of obtainings
approximations of the image. Instead, quadtree skeletons are exact
representations of the image and are used because they are observed
to yield space efficiency and a decreased sensitvity to shifts in
contrast with the quadtree. The QMAT can be used as the underlying
representation when solving most problems that can be solved by
using a quadtree. An algorithm is presented for the computation of
the QMAT of a given quadtree by only examining each BLACK node's
adjacent and abutting neighbors. Analysis of the algorithm reveals
an average execution time proportional to the complexity of the
image, i.e., the number of BLACK blocks.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 8
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Claude Puech , Hussein Yahia, Quadtrees, octrees, hyperoctrees: a unified analytical approach to tree data structures used in graphics, geometric modeling and image processing, Proceedings of the first annual symposium on Computational geometry, p.272-280, June 05-07, 1985, Baltimore, Maryland, United States
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INDEX TERMS
Primary Classification:
E.
Data
E.1
DATA STRUCTURES
Subjects:
Trees
Additional Classification:
I.
Computing Methodologies
I.2
ARTIFICIAL INTELLIGENCE
I.2.10
Vision and Scene Understanding
Subjects:
Representations, data structures, and transforms;
Shape;
Motion
I.3
COMPUTER GRAPHICS
I.3.5
Computational Geometry and Object Modeling
Subjects:
Geometric algorithms, languages, and systems;
Curve, surface, solid, and object representations
I.4
IMAGE PROCESSING AND COMPUTER VISION
I.4.2
Compression (Coding)
Subjects:
Exact coding**
I.4.7
Feature Measurement
Subjects:
Size and shape
General Terms:
Algorithms
Keywords:
medial axis transforms,
quadtrees,
shape,
size,
skeletons
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