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图像阈值化的自适应粗糙熵方法

DOI: 10.11834/jig.20140101

Keywords: 图像分割,粗糙集,粗糙粒度,粗糙熵,过渡区阈值化

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Abstract:

目的图像阈值化将灰度图像转换为二值图像,被广泛应用于多个领域。因实际工程应用中固有的不确定性,自动阈值选择仍然是一个极具挑战的课题。针对图像自动阈值化问题,提出了一种利用粗糙集的自适应方法。方法该方法分析了基于粗糙集的图像表示框架,建立了图像粗糙粒度与局部灰度标准差的相互关系,通过最小化自适应粗糙粒度准则获得最优的划分粒度。进一步在该粒度下构造了图像目标和背景的上下近似集及其粗糙不确定度,通过搜索灰度级最大化粗糙熵获得图像最优灰度阈值,并将图像目标和背景的边界作为过渡区,利用其灰度均值作为阈值完成图像二值化。结果对本文方法通过多个图像分3组进行了实验比较,包括3种经典阈值化方法和一种利用粗糙集的方法。其中,本文方法生成的可视化二值图像结果远远优于传统粗糙集阈值化方法。此外,也采用了误分率、平均结构相似性、假阴率和假阳率等指标进一步量化评估与比较相关实验结果。定性和定量的实验结果表明,本文方法的图像分割质量较高、性能稳定。结论本文方法适应能力较好,具有合理性和有效性,可以作为现有经典方法的有力补充。

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