Image fusion is a method of imparting all relevant and complementary image details into a single composite image extracted from images of same source or various sources. This paper proposes a fusion method based on segmented regions of source images which are obtained by a fuzzy C-Means clustering algorithm. Robust clustering is exhibited by Fuzzy C-means algorithm by assigning fuzzy membership...

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze th...

A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning diicult clustering problems show that the proposed approach is very successful in genera...

This paper presents an integration framework for image segmentation. The proposed method is based on Fuzzy c-means clustering (FCM) and level set method. In this framework, firstly Chan and Vese’s level set method (CV) and Bayes classifier based on mixture of density models are utilized to find a prior membership value for each pixel. Then, a supervised kernel based fuzzy c-means clustering (SK...

In many applications, denoising is necessary since point-sampled models obtained by laser scanners with insufficient precision. An algorithm for point-sampled surface is presented, which combines fuzzy c-means clustering with mean shift filtering algorithm. By using fuzzy c-means clustering, the largescale noise is deleted and a part of small-scale noise also is smooth. The cluster centers are ...