Enayatifar, R. and Meybodi, M.R. (2009) Adaptive Edge Detection via Image Statistic Features and Hybrid Model of Fuzzy Cellular Automata and Cellular Learning Automata. International Conference on Information and Multimedia Technology, Jeju Island, 16-18 December 2009, 273-278.

has been cited by the following article:

• TITLE: Gray Level Image Edge Detection Using a Hybrid Model of Cellular Learning Automata and Stochastic Cellular Automata

  AUTHORS: Nasim Vatani, Rasul Enayatifar

  KEYWORDS: Cellular Learning Automata, Stochastic Cellular Automata, Edge Detection, Image Processing, Statistic Feature

  JOURNAL NAME: Open Access Library Journal, Vol.2 No.1, January 23, 2015

  ABSTRACT: The mathematical model that aims at determining points in an image at which the image brightness suddenly changes is called edge detection. This study aims to propose a new hybrid method for edge detection. This method is based on cellular learning automata (CLA) and stochastic cellular automata (SCA). In the first part of the proposed method, statistic features of the input image are hired to have primary edge detection. In the next step CLA and SCA are employed to amplify pixels situated on edge and castrate those pixels which are part of the image background. The simulation results are conducted to prove proposed method performance and these results suggest that the proposed method is more efficient in finding edges and outperforms the existing edge detection algorithms.