基于超熵和模糊集理论的带钢表面缺陷分割

Steel strip surface defect segmentation based on excess entropy and fuzzy set theory

由于冷轧带钢表面缺陷图像中存在过渡区,在图像分割过程中既要利用灰度信息也要利用空间结构信息才能取得好的分割效果。因此,本文研究了信息熵中的超熵以及模糊集理论,根据超熵可以用来测度图像的空间结构,模糊集可以描述出图像灰度过渡区的特性,提出了一种基于超熵和模糊集理论的图像分割算法。结合超熵和模糊集理论构建出模糊超熵,通过计算图像的最大模糊超熵所对应的最优隶属度函数参数组合确定了分割阈值,并利用该阈值完成图像分割。将该算法与Ostu以及一维最大模糊熵分割算法相比较,结果显示,本文算法能够准确地从背景中提取缺陷,有效地抑制了过分割现象。利用提出的误分割率和有效信息率对分割后的图像进行定量评价,结果表明,用本文算法分割后的图像有效信息率在3种方法中最高,均在82.7%以上,同时误分割率均低于2.1%。

Because of the existence of transition zones in a cold rolling strip surface defect image,gray information and spatial structure information should be combined to segment images to obtain better image results.Therefore,the excess entropy of information entropy and fuzzy set theory were researched.As the excess entropy could be used to measure spatial structure of an image and the characteristic of image gray transition zone could be described well by the fuzzy set,an image threshold segmentation algorithm based on maximal fuzzy excess entropy was proposed.The fuzzy excess entropy was built by the combination of excess entropy and fuzzy set theory and the threshold was determined by the best membership function parameter combination according to the maximal fuzzy excess entropy value.Then,the image was segmented by using the threshold.Finally,the algorithm was compared with Ostu and 1D maximal fuzzy entropy segmentation algorithms.The experiment indicates that the proposed algorithm can extract the defect from a background exactly and can constrain the over-segmentation effectively.The quantificational evaluation of segmented image was performed by the wrong segmentation rate and effective information rate,and the results show that the effective information rate of the algorithm is higher than 82.7%,which is the maximal one among three methods.Meanwhile the wrong segmentation rate is below 2.1%.