Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.

模糊集FScom在台风灾害等级评判中的应用

模糊集FScom在传统模糊集的基础上提出了三种否定关系:矛盾否定关系、对立否定关系以及中介否定关系,对处理模糊知识信息更具有实用性。以模糊集FScom及其λ-中介否定集为理论基础,给出了一种基于FScom模糊综合评判方法的灾害等级评判体系,将此体系应用到台风灾害等级评判中。

汉语否定句全量宾语的语义解读

本文在语料库调查的基础上探讨汉语否定句全量宾语的语义解读问题。首先指出汉语否定句宾位全量成分存在部分否定与全量否定两种语义解读的可能,然后通过统计数据说明汉语否定句全量宾语的实际语义解读受到全称量化词的取值、否定词的取值、核心谓语的语义类型及否定词前特定副词的有无等因素的影响,最后在跨语言比较的大背景下指出汉语"否定词+全量宾语"之所以能相对自由地表达全量否定与汉语量化词库中全量否定词阙如以及任指量化词晚起且不发达等特点有关。