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A Model-based Neural Network for Edge Characterization by Hau-San Wong; Terry Caelli; Ling Guan is a Computer Science article available to read on EtoBox.

What is A Model-based Neural Network for Edge Characterization about?

In this paper, we investigate the feasibility of characterizing signi"cant image features using 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 signi"cant features on an image, and then incorporate these selected features directly as training examples for the network. As a "rst step, we consider the problem of the characterization of edges, which are usually regarded as signi"cant image features by humans. Unlike conventional edge detection schemes where decision thresholds have to be speci"ed, 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, and which thus allow automatic generation of the "nal binary edge map without further parameter adjustments. Experiments have con"rmed that the resulting network is capable of generalizing this previously acquired knowledge to identify important edges in images not included in the training set. In particular, one of the important attributes characterizing the current approach is its robustne

Who reads A Model-based Neural Network for Edge Characterization?

Author

Hau-San Wong; Terry Caelli; Ling Guan

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0031-3203)

Published

2000

Language

EN

Field

Computer Science (Physical Sciences)