Abstract
It is a challenging topic to perform pattern reconstruction from a unit-width skeleton, which is obtained by a parallel thinning algorithm. The bias skeleton yielded by a fully-parallel thinning algorithm, which usually results from the so-called hidden deletable points, will result in the difficulty of pattern reconstruction. In order to make a fully-parallel thinning algorithm pattern reconstructable, a newly-defined reconstructable skeletal pixel (RSP) including a thinning flag, iteration count, as well as reconstructable structure is proposed and applied for thinning iteration to obtain a skeleton table representing the resultant thin line. Based on the iteration count and reconstructable structure associated with each skeletal pixel in the skeleton table, the pattern can be reconstructed by means of the dilating and uniting operations. Embedding a conventional fully-parallel thinning algorithm into the proposed approach, the pattern may be over-reconstructed due to the influence of a biased skeleton. A simple process of removing hidden deletable points (RHDP) in the thinning iteration is thus presented to reduce the effect of the biased skeleton. Three well-known fully-parallel thinning algorithms are used for experiments. The performances investigated by the measurement of reconstructability (MR), the number of iterations (NI), as well as the measurement of skeleton deviation (MSD) confirm the feasibility of the proposed pattern reconstruction approach with the assistance of the RHDP process.
Highlights
The purpose of thinning is to remove a large number of unwanted points for extracting the isotropic thinned skeleton, which shall retain the pattern’s geometrical shape features for computers to efficiently perform image-related tasks, such as character recognition [1,2], fingerprint identification [3], posture recognition [4], retinal image analysis [5], and so on
The goal of this paper is to propose a pattern-reconstructable strategy embedded in a rule-based thinning so that a good thinning result can be maintained, as well as the original pattern can be reconstructed from the built thin line information
In order to avoid the unwanted branches as suffered by Jang and Chin’s method [12], in this study, we present a pattern-reconstructable strategy and hidden-deletable-point removal method embedded in a fully-parallel thinning, where the former can extract the reconstructable information during the fully-parallel thinning process, and the latter can help with reducing the effect of the bias skeleton, further improving the reconstruction
Summary
The purpose of thinning is to remove a large number of unwanted points for extracting the isotropic thinned skeleton, which shall retain the pattern’s geometrical shape features for computers to efficiently perform image-related tasks, such as character recognition [1,2], fingerprint identification [3], posture recognition [4], retinal image analysis [5], and so on. Rule-based thinning applies the thinning templates iteratively to remove the contour points until the unit-width skeleton is obtained. In accordance with the operating manner, rule-based thinning algorithms can be further divided into sequential [6] and parallel [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] thinning. Sub-cycles used in an iteration are often adopted for developing a sub-cycle-based parallel thinning, where the
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