基于PCNN的图像二值化及分割评价方法

Image Binarization Based on PCNN and Corresponding Segmentation Evaluation Method

针对目前图像二值化方法通用性不强、自适应阈值选取难,以及单一图像分割评价缺乏可靠性的问题,对基于脉冲耦合神经网络(PCNN)的图像二值化方法及其参数选择进行了研究,提出了一种综合考虑多种评价准则的用于评价图像分割效果的方法.实验结果表明:基于PCNN的二值化方法非常适合于各类图像的分割,具有分割精度高的特点;与单一评价方法相比,文中的综合评价方法能够更加客观准确地反映分割方法的分割效果.

In order to overcome the weak adaptability and the difficulty in selecting the adaptive threshold of the traditional image binarization and to improve the reliability lacking in the traditional single evaluation of image seg- mentation, an image binarization method based on pulse-coupled neural network （PCNN） is investigated, and the corresponding parameters are selected. Afterwards, a composite segmentation evaluation method comprehensively considering various evaluation criteria is proposed. Experimental results show that the PCNN-based image binariza- tion method is of high accuracy and is suitable for the segmentation of varied images, and that, as compared with the traditional single evaluation methods, the proposed composite method can evaluates the performances of segmen- tation algorithms more objectively and accurately.