图像边缘检测中的自适应模糊熵算法

2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control Pub Date : 2012-12-08 DOI:10.1109/IMCCC.2012.91

Zhou Jihong, Lu Jun, Lin Xianqing

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引用次数: 1

摘要

为了提高图像边缘检测的有效性，提出了一种基于模糊熵的加权图像边缘检测算法。该方案首先定义了三个图像度量来表示图像边缘:有序度量、指向性度量和结构度量。然后基于模糊熵从图像中提取这些指标，并对三个指标进行加权计算边缘决策的置信度，其中加权因子可以充分考虑指标对边缘检测的影响。最后，算法采用非极大值图像抑制和动态阈值抑制图像伪边缘，进一步优化。实验结果表明，在保留更多图像细节的情况下，边缘检测具有更好的性能。

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An Adaptive Fuzzy Entropy Algorithm in Image Edge Detection

To improve the effectiveness of image edge detection, this paper proposed a weighted image edge detection algorithm based on fuzzy entropy. The scheme firstly defines three image metrics to represent image edge: orderliness measure, directivity measure and structural measure. Then it extracts these metrics from images based on fuzzy entropy, and weights three metrics to calculate a confidence degree for edge decision, in which the weighted factors may fully consider the metric impact to detect edges. Finally the algorithm adopts non-maximal image suppression with dynamic threshold to inhibit image pseudo-edge for further optimization. Experiment results illustrate better performance in edge detection with maintaining more image details.

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2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control

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