New Single-Pass Algorithm for Parallel Thinning

Choy, Sheung-On; Choy, Clifford Sze-Tsan; Siu, Wan-Chi

doi:10.1006/cviu.1995.1042

Cited by 13 publications

(4 citation statements)

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“…The algorithms proposed by T. Pavlidis in 1981 [31,32] [9] do not produce a crucial retraction after a single step of execution. After a quick examination of some configurations detected by our verification program, we found that none of these algorithms does guarantee topology preservation.…”

mentioning

confidence: 99%

Two-Dimensional Parallel Thinning Algorithms Based on Critical Kernels

Bertrand

Couprie

2008

J Math Imaging Vis

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Critical kernels constitute a general framework settled in the category of abstract complexes for the study of parallel thinning in any dimension. The most fundamental result in this framework is that, if a subset Y of X contains the critical kernel of X, then Y is guaranteed to have "the same topology as X". Here, we focus on 2D structures in spaces of two and three dimensions. We introduce the notion of crucial pixel which permits to make a link with the framework of digital topology. Thanks to simple local characterizations, we are able to express thinning algorithms by the way of sets of masks. We propose several new parallel algorithms, which are both fast and simple to implement, to obtain symmetrical or non-symmetrical skeletons of 2D objects in 2D or 3D grids. We prove some properties of these skeletons, related to topology preservation, to minimality and to the inclusion of the topological axis which may be seen as a generalization of the medial axis. We also show how to use critical kernels in order to prove very simply the topological soundness of existing thinning schemes. At last, we make clear the link between critical kernels, minimal non-simple sets, and P-simple points.

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confidence: 99%

Two-Dimensional Parallel Thinning Algorithms Based on Critical Kernels

Bertrand

Couprie

2008

J Math Imaging Vis

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“…This method prioritizes the possibility of restoration rather than connectivity preservation of the object [5] . Furthermore iterative thinning is classified into sequential [6] and parallel methods [7][8][9][10][11][12][13]. In every iteration steps, the sequential thinning algorithm applies all previous computed results to a present computing step.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the definition and requirements of thinning are also different. In spite of the requirement of diversity, the following requirements are generally and common to all algorithms [8], [14][15][16][17] . Requirement 1: Connected image regions should thin to connected line structures (connectivity preservation); Requirement 2: Approximate end-line locations should be maintained (no excessive erosion); Requirement 3: The thinning results should at a minimum be 8-connected (unit-width skeleton); Requirement 4: The thinning results should approximate the medial lines (medial line approximation); and Requirement 5: Extraneous spurs caused by thinning should be minimized (boundary noise immunity).…”

Section: Introductionmentioning

confidence: 99%