Optimization of image processing techniques using Neural Networks –A Review

This paper reviews the application of artificial neural networks in image preprocessing. Neural networks, especially uses feed-forward neural networks, Kohonen feature maps, back-propagation neural networks, multi-layer perception neural networks and Hopfield neural networks. The various applications are categorized into a novel two-dimensional taxonomy. One dimension specifies the type of task performed by the algorithm, preprocessing, data reduction or feature extraction, segmentation, object recognition, image understanding and optimization. The other dimension captures the abstraction level of the input data processed by the algorithm that is pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterization. Each of the six types of tasks poses specific constraints to a neural-based approach. 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. By this survey, the paper try to answer what the major strengths and weakness of applying neural networks for image processing would be.