Image Processing Using Artificial Networks and Neural Networks

Abstract

Image processing using artificial neuronal networks (ANN) has been successfully used in various fields of activity such as geotechnics, civil engineering, mechanics, industrial surveillance, defense department, automatics and transport. Image preprocessing, date reduction, segmentation and recognition are the processes used in managing images with ANN. An image can be represented as a matrix, each element of the matrix containing color information for a pixel. The matrix is used as input data into the neuronal network. The small dimensions of the images, to easily and quickly help learning, establish the size of the vector and the number of input vectors. Artificial neural networks (ANNs) are very general function approximates which can be trained based on a set of examples. Given their general nature, ANNs would seem useful tools for nonlinear image processing. A range of experimental results lead to the conclusion that ANNs are mainly applicable to problems requiring a nonlinear solution, for which there is a clear, unequivocal performance criterion, i.e. high-level tasks in the image processing chain (such as object recognition) rather than low-level tasks.