A comment on “a fast parallel algorithm for thinning digital patterns”

Lu, Hsin-Min; Wang, P. S. P.

doi:10.1145/5666.5670

Cited by 124 publications

(16 citation statements)

References 2 publications

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“…An iteration step of directional (or border sequential, or subiteration) algorithms is divided into a number of successive subiterations, where only border points of a certain kind can be deleted. Note that 2-subiteration (Zhang and Suen, 1984;Lü and Wang, 1986;Chen and Hsu, 1989;Gou and Hall, 1989;Zhang and Wang, 1996) and 4-subiteration (Stefanelli and Rosenfeld, 1971;Rosenfeld, 1975;Davies and Plummer, 1981;Arcelli et al, 1994) algorithms have been developed for this task. The support of their parallel reductions is usually 3 3 3.…”

Section: Variations On Thinning Algorithmsmentioning

confidence: 99%

Topology preserving parallel thinning algorithms

Németh

Palágyi

2011

Int J Imaging Syst Tech

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Thinning is an iterative object reduction technique for extracting medial curves from binary objects. During a thinning process, some border points that satisfy certain topological and geometric constraints are deleted in iteration steps. Parallel thinning algorithms are composed of parallel reduction operators that delete a set of object points simultaneously. This article presents 21 parallel thinning algorithms for (8,4) binary pictures that are derived from the sufficient conditions for topology preservation accommodated to the three parallel thinning approaches.

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Section: Variations On Thinning Algorithmsmentioning

confidence: 99%

Topology preserving parallel thinning algorithms

Németh

Palágyi

2011

Int J Imaging Syst Tech

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“…Such parallelism in skeletonizing algorithms has been broadly studied (see, e.g., [21,32,43,45]). The development of new hardware architectures has also contributed to new parallel implementations of these algorithms [19,24,26].…”

Section: Introductionmentioning

confidence: 99%

Skeletonizing Digital Images with Cellular Automata

Díaz-Pernil

Peña-Cantillana

Gutiérrez–Naranjo

2014

Emergence, Complexity and Computation

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Abstract. The skeletonization of an image consists of converting the initial image into a more compact representation. In general, the skeleton preserves the basic structure and, in some sense, keeps the meaning. The most important features concerning a shape are its topology (represented by connected components, holes, etc.) and its geometry (elongated parts, ramifications, etc.), thus they must be preserved. Skeletonization is usually considered as a pre-processing step in pattern recognition algorithms, but its study is also interesting by itself for the analysis of line-based images such as texts, line drawings, human fingerprints classification or cartography.Since the introduction of the concept by Blum in 1962 under the name of medial axis transform, many algorithms have been published in this topic and there are many different approaches to the problem, among them the ones based on distance transform of the shape and skeleton pruning based on branch analysis. In this chapter, we focus on how the skeletonization of an image can be studied in the Cellular Automata framework and, as a case study, we consider in detail the Guo and Hall skeletonizing algorithm.

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“…This is a modification of the original Zhang and Suen algorithm, to better cope with 2 × 2 squares and diagonal lines, similar to the one proposed by Lü and Wang [7] or Chen and Hsu [5].…”

Section: Introductionmentioning

confidence: 99%