基于多特征分步模糊推理的边缘检测算法

基于Mamdani模糊推理的边缘检测，将多个特征作为整体进行一步推理，但不同特征难以兼顾对模糊边缘的敏感和噪声的抑制，导致算法鲁棒性下降. 为此，该文提出了一种基于重要性加权的分步推理算法，根据各特征对边缘敏感和噪声抑制的重要性分步进行模糊推理，并加权每步推理结果作为边缘隶属度. 并提出了一种基于面积近似的重心法改进算式，能更好兼顾解模糊的准确性和实时性. 实验结果表明了该算法的准确性、鲁棒性和实时性.

Edge Detection Based on Multi-Features and Step-by-Step Fuzzy Inference

Edge detection based on Mamdani fuzzy inference regards all features as a whole to carry on one-step inference. However, this will decrease the robustness of algorithm since different features are difficult to guarantee both the sensibility for fuzzy edge and the suppression for noise. This paper presents a step-by-step processing scheme based on multi-features importance-weighted fuzzy inference. According to the importance degree of each feature in the edge representation and noise suppression, the proposed method carries on a step-by-step fuzzy inference and weights all inference results to obtain the edge membership. In addition, this paper presents an improved centroid method based on area approximation, which has higher precision and real-time in defuzzification. The experiment results show the precision, robustness and real-time of the proposed algorithm.