In this paper, we investigate the feasibility of characterizing significant image edges using a model-based neural network with modular architecture. Instead of employing traditional mathematical models for characterization, we ask human users to select what they regard as significant features on an image, and then incorporate these selected edges directly as training examples for the network. Unlike conventional edge detection schemes where decision thresholds have to be specified, the current NN-based edge characterization scheme implicitly represents these decision parameters in the form of network weights which are updated during the training process. Experiments have confirmed that the resulting network is capable of generalizing this previously acquired knowledge to identify important edges in images not included in the training set. Most importantly, the current approach is very robust against noise contaminations, such that no re-training of the network is required when it is applied to noisy images.
['Hau-San Wong', 'Terry Caelli', 'Ling Guan']
Edge characterization using a model-based neural network