Abstract：Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented .Firstly, a definition of fuzzy partition entropy is proposed after introducing the concept of fuzzy probability and fuzzy partition, The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm。Secondly, based on the conditional probabilities and the fury partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained。Lastly, an edge-enhancing procedure is executed on the edge image .The experimental results show that the proposed approach performs well。
Key words :edge detection ;fuzzy entropy ;image segmentation ;fuzzy partition
摘要:基于信息論中最大熵原理，提出了一種新的模糊邊緣檢測(cè)算法。首先介紹了模糊概率、用條件概率與條件熵定義模糊劃分熵的概念以及模糊劃分的原理。算法利用了自然劃分以及梯度圖像模糊劃分的關(guān)系，在條件概率與模糊劃分熵的基礎(chǔ)上，通過(guò)最大模糊熵原則實(shí)現(xiàn)圖像分割中最優(yōu)閾值的自動(dòng)提取，從而實(shí)現(xiàn)圖像的邊緣檢測(cè)。對(duì)不同測(cè)試圖像的邊緣檢測(cè)結(jié)果進(jìn)行比較，表明了該算法的有效性。
關(guān)鍵詞：邊緣檢測(cè)；模糊熵；圖像分割；模糊劃分