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Issue 5, Volume 4, May 2007
Print ISSN: 1109-9518
E-ISSN: 2224-2902







Title of the Paper: The Possibilistic Correlation-Dependent Fusion Methods for Optical Detection


Authors: Shaheera Rashwan

Abstract: Multi sensor fusion is an important component of applications for systems that use correlated data from multiple sensors to determine the state of a system. As the state of the system being monitored and many sensors are affected by the environmental conditions changing with time, the multi sensor fusion requires a correlation-dependent approach. The behavior of this approach should vary according to the correlation parameter. In this paper, we compare our possibilistic correlation-dependent fusion approach (PCDF) with the possiblistic combiner Dempster-Shafer. We focus in this paper on the mathematical background of this approach so that it can be used in many useful applications. We use time-series infrared images of landmines buried in different types of soil.

Keywords: Image Fusion, Correlation, T-Norm, Dempster Shafer, time-series images of buried mines.

Title of the Paper: Edge Detection of Images based on Cloud Model Cellular Automata


Authors: Zhang Ke, Yuan Jin-Sha, Yang Xue-Ming

Abstract: In order to resolve the problems of edge detection algorithm of images based on fuzzy seasoning or cellular automata, a new improved edge detection algorithm of images based on cloud model cellular automata is presented. This method uses direction information and edge order information as edge characteristic information, uses cloud model to inference these information, then gives accurate feedback information got from inference results to direction information measure and direction edge order measure, and detects edge by automatic evolution of cellular automata. Finally, experiments are put forward, this algorithm has powerful ability in exiguous edge detection, and it is a promising and applied image processing algorithm.

Keywords: Edge Detection; Cloud Model; Cellular Automata; Multi-information Fusion; Cloud Reasoning

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