一种鲁棒的模式识别并行细化算法

P. Tarábek
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引用次数: 17

摘要

细化算法在模式识别和图像分析中已经应用了很长时间。他们减少一个二进制数字模式,以获得一个单位宽度的骨架,保留几何和拓扑性质。这些属性对于字符、手写、指纹、交通基础设施等的鲁棒识别非常重要。在流行的Zhang和Suen算法的基础上,提出了一种鲁棒并行细化算法。ZS算法在连通性和对边界噪声的不敏感性方面都非常好,但它倾向于去除对角线段和整个2×2方形图案,并且不产生单位宽度骨架。实验结果表明,该方法保留了ZS算法的优良特性,并通过加入附加条件来识别关键模式,并通过后处理步骤去除所有冗余像素,从而产生1像素厚的骨架,从而克服了ZS算法的缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A robust parallel thinning algorithm for pattern recognition
Thinning algorithms have been used in pattern recognition and image analysis for a long time. They reduce a binary digital pattern to obtain a unit width skeleton which retains geometrical and topological properties. These properties are important for robust recognition of characters, handwritings, fingerprints, transportations infrastructure and other. A robust parallel thinning algorithm based on the popular Zhang and Suen algorithm is presented. The ZS algorithm is very good in respect to both connectivity and insensitivity to boundary noise but it tends to remove diagonal line segments and whole 2×2 square patterns, and does not produce a unit width skeleton. The experimental results show that the proposed method preserves good properties of ZS algorithm and it overcomes the disadvantages by incorporating additional conditions for identifying the crucial patterns and by applying a post-processing step that removes all redundant pixels so the one pixel thick skeleton is produced.
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