利用变精度粗糙熵的图像分割算法

An image segmentation algorithm using variable precision rough entropy

图像分割是把图像分成若干个特定的、具有独特性质的区域并提取感兴趣目标的技术和过程,其结果将直接影响到目标物特征提取和描述,以及更进一步的目标物识别、分类和图像理解。因图像信息的复杂性和相关性,图像分割会出现不确定性和模糊性。图像用变精度粗糙集表示,结合粗糙熵和粒子群优化算法,提出变精度粗糙熵的图像分割算法,求出最大粗糙熵对应的最佳分割阈值,再用二值分割法对图像进行分割。实验结果表明,所提算法优于传统的单阈值分割法,且具有一定实用性和灵活性。

Image segmentation is a technique and process of dividing an image into a number of specific,unique areas and extracting the target of interest.Segmentation results directly affect target feature extraction and description,further target identification,classification and image understanding.Due to the complexity and relevance of image information,there is uncertainty and fuzziness in image segmentation.We use the variable precision rough set to represent the image,combining rough entropy and particle swarm optimization algorithm,propose an image segmentation algorithm based on variable precision rough entropy.We obtain the optimal segmentation threshold corresponding to the maximum rough entropy and then divide the image by the binary segmentation method.Experimental results show that the proposed algorithm is superior to the traditional single threshold segmentation method,and has certain practicability and flexibility.