Learning optimal linear filters for early vision

Abstract

The authors describe experiments in which the rescaling back-propagation learning algorithm was used to learn sets of linear filters for the task of determining the orientation and location of edges to subpixel accuracy. A model of edge formation was used to generate input-output pairs for each iteration of the training process. The desired output included determining the interpolated location and orientation of the edge. >