Image segmentation using cloud model and data field

An image thresholding method is proposed to select the optimal threshold for image segmentation. The data field is introduced to map the original image from grayscale space to the corresponding potential space by the proposed method, and the relative and the absolute data fields are produced with two different mass functions. Then, by considering the features of the data fields and combining the global and the local statistical characteristics of the image, a potential threshold for data field can be yielded. Next, the pixels are divided into possible backgrounds and objects, which are represented with background and object cloud models generated by backward cloud generator. According to the membership degrees of pixels over two cloud models, the final result of image thresholding is obtained by maximum determination method. It is indicated by the experiments that the proposed method yields accurate and robust result, and it is reasonable and effective.