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 im...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.展开更多
In this paper I present a syntactic approach to modeling the interactive knowledge of rationality in finite games of perfect information. This approach allows for a more transparent interpretation. In particular, we h...In this paper I present a syntactic approach to modeling the interactive knowledge of rationality in finite games of perfect information. This approach allows for a more transparent interpretation. In particular, we have the intuitive picture of viewing knowledge as the input and decisions as the output of a player＇s deliberation. This distinction is blurred in the semantic state-space approach.展开更多
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘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.
文摘In this paper I present a syntactic approach to modeling the interactive knowledge of rationality in finite games of perfect information. This approach allows for a more transparent interpretation. In particular, we have the intuitive picture of viewing knowledge as the input and decisions as the output of a player＇s deliberation. This distinction is blurred in the semantic state-space approach.
