A Modified Thinning Framework Against Noise

Yuanxu Liu, Jun Ma, X. Ren, V. Tsviatkou, Hao Li
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Abstract

We proposed a modified thinning framework that based on the scale space technique to automatically extract skeletons from images without manual-tuning. The proposed framework can increase the robustness of the thinning algorithm, it not only can suppress the boundary noise, but also can alleviate the inner noise. These two types of noise generally cause the appearance of the abundant of the unwanted branches in the outcome of the thinning algorithm, which arise the difficulties of the later recognition or matching process in skeleton. The experiment has proved the proposed framework has better performance when comparing with the other existing methods.
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一种改进的抗噪细化框架
我们提出了一种改进的基于尺度空间技术的细化框架,可以在不需要手动调整的情况下自动提取图像中的骨架。该框架不仅可以抑制边界噪声,还可以减轻内部噪声,提高了稀疏算法的鲁棒性。这两种类型的噪声通常会导致稀疏算法的结果中出现大量不需要的分支,从而给后续的骨架识别或匹配过程带来困难。实验结果表明,该框架与现有方法相比具有更好的性能。
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