Image processing with neural networks - a review

Journal article (2002)

Authors

M Egmont-Petersen External organisation
D. de Ridder ImPhys/Quantitative Imaging
H Handels External organisation
Research Group
ImPhys/Quantitative Imaging
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Published Date

2002

Language

Undefined/Unknown

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Research Group

ImPhys/Quantitative Imaging

Abstract

We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hop1eld neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension speci1es the type of task performed by the algorithm: preprocessing, data reduction=feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterisation. Each of the six types of tasks poses speci1c constraints to a neural-based approach. These speci1c conditions are discussed in detail. A synthesis is made of unresolved problems related to the
application of pattern recognition techniques in image processing and speci1cally to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.
Keywords: Neural networks; Digital image processing; Invariant pattern recognition; Preprocessing; Feature extraction; Image compression; Segmentation; Object recognition; Image understanding; Optimization