1990
DOI: 10.1016/0020-0255(90)90008-x
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Octagonal distances for digital pictures

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Cited by 40 publications
(29 citation statements)
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“…Several error functions minimizing the asymptotic maximum difference of two balls of equal radius using a distance based on periodic neighbourhood sequences and a Euclidean sphere, respectively, is presented in [5,8,30] (Z 2 ), [18] (Z 3 ). An investigation of optimal non-periodic neighbourhood sequences in Z 2 with optimal sequences also for finite distances is found in [12].…”
Section: Introductionmentioning
confidence: 99%
“…Several error functions minimizing the asymptotic maximum difference of two balls of equal radius using a distance based on periodic neighbourhood sequences and a Euclidean sphere, respectively, is presented in [5,8,30] (Z 2 ), [18] (Z 3 ). An investigation of optimal non-periodic neighbourhood sequences in Z 2 with optimal sequences also for finite distances is found in [12].…”
Section: Introductionmentioning
confidence: 99%
“…The examination of hyperspheres with neighbourhood sequences started in [3] in two dimension with periodic neighbourhood sequences. In higher dimension hyperspheres were also analyzed using only constant neighbouring criteria in [4].…”
Section: Hyperspheres In Nd and ∞Dmentioning
confidence: 99%
“…In [3] Das examined the digital circles in two dimensions, in [1] Danielsson analyzed the digital spheres in 3D digital space with periodic neighbourhood sequences. In [4] hyperspheres based only on simple neighbourhood relations were investigated in arbitrary finite-dimensional spaces, while in [7] Hajdu described hyperspheres in an arbitrary finite dimension with generalized neighbourhood sequences.…”
Section: Introductionmentioning
confidence: 99%
“…This can be generalized as proposed in [35] to more complicated combinations of D 4 and D 8 . These distances, called generalized octagonal distances, are metrics only when the simple path generated DTs are combined in a proper way [16]. Using the series (D 4 ,…”
Section: Comparison Between Different Distance Transformsmentioning
confidence: 99%