A new distance measure for binary images

A distance measure, called the generalized Euclidean distance, is developed for binary images to take into account perceptual distortions. Based on this distance measure, a type of transformation is devised to ensure that the generalized Euclidean distance of two images is the same as the Euclidean distance of two transformed images. A set of transformed images is then used to train and test a feed-forward neural network for handwritten numeral recognition. It is shown that the recognition rate is significantly improved by incorporating human perception into the neural network, and that the transformation step can be merged into the trained neural network so that no transformation is required during the recognition stage.<<ETX>>