International Journal of Man-Machine Studies
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Cognitive mapping and certainty neuron fuzzy cognitive maps
Information Sciences: an International Journal
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
On causal inference in fuzzy cognitive maps
IEEE Transactions on Fuzzy Systems
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
A Hopfield Neural Network for Image Change Detection
IEEE Transactions on Neural Networks
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This paper outlines a neural network model based on the Fuzzy Cognitive Maps (FCM) framework for solving the automatic image change detection problem. Each pixel in the reference image is assumed to be a node in the network. Each node has associated a fuzzy value, which determines the magnitude of the change. Each fuzzy value is updated by a trade-off between the influences received from the fuzzy values from other neurons and its own fuzzy value. Classical approaches in the literature have been designed assuming that the mutual influences between two nodes are symmetric. The main finding of this paper is the assumption that mutual influences could not be symmetric. This non symmetric relationship can be embedded by the FCM paradigm. The performance of the proposed method is illustrated by comparative analysis against some recent image change detection methods.