%0 Journal Article %1 EgmontPetersen20022279 %A Egmont-Petersen, M. %A de Ridder, D. %A Handels, H. %D 2002 %J Pattern Recognition %K coop_bio neural_networks review %N 10 %P 2279 - 2301 %R http://dx.doi.org/10.1016/S0031-3203(01)00178-9 %T Image processing with neural networks—a review %U http://www.sciencedirect.com/science/article/pii/S0031320301001789 %V 35 %X 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 Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies 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 specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.

@article{EgmontPetersen20022279, 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 Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies 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 specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments. }, added-at = {2016-05-04T13:16:05.000+0200}, author = {Egmont-Petersen, M. and de Ridder, D. and Handels, H.}, biburl = {https://www.bibsonomy.org/bibtex/20eec59324c597f7bb5d6caf5c1d71163/dallmann}, doi = {http://dx.doi.org/10.1016/S0031-3203(01)00178-9}, interhash = {0187ac8d602f6521d1abafdf5e7f619a}, intrahash = {0eec59324c597f7bb5d6caf5c1d71163}, issn = {0031-3203}, journal = {Pattern Recognition }, keywords = {coop_bio neural_networks review}, number = 10, pages = {2279 - 2301}, timestamp = {2016-05-04T13:16:05.000+0200}, title = {Image processing with neural networks—a review }, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001789}, volume = 35, year = 2002 }