Edge detection using zero crossings of directional derivatives of a random field model

An edge detector using the first and second directional derivatives of a random field model is described. The method consists of representing the pixels in a window by a 2 - D causal autoregressive (AR) model, whose parameters are adaptively estimated using a Kalman filter. Due to the modelling assumption, the directional derivates are functions of AR parameter estimates. An edge is detected if the second derivate in the direction of the estimated gradient is negatively sloped, the first derivatives and a local estimate of sample variance are over some threshold values. We illustrate the performance of the edge detector using real image examples.