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.

[1]  Ugo Montanari,et al.  On the optimal detection of curves in noisy pictures , 1971, CACM.

[2]  M. Basseville,et al.  Edge detection using sequential methods for change in level--Part I: A sequential edge detection algorithm , 1981 .

[3]  Ahmad Salahi.  IMAGE SEGMENTATION BY STATISTICAL EDGE DETECTION AND SEQUENTIAL EDGE GROWING. , 1979 .

[4]  P. Van Remoortere,et al.  Dynamic stochastic models from empirical data , 1979 .

[5]  J. Woods,et al.  Estimation and identification of two dimensional images , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[6]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  K. Kunz,et al.  Numerical analysis , 1957 .

[8]  R. Haralick.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Larry S. Davis,et al.  A survey of edge detection techniques , 1975 .

[10]  R. Kashyap,et al.  Dynamic Stochastic Models from Empirical Data. , 1977 .

[11]  Manfred H. Hueckel.  An Operator Which Locates Edges in Digitized Pictures , 1971, J. ACM.

[12]  Robert M. Haralick,et al.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[14]  Arnold K. Griffith,et al.  Mathematical Models for Automatic Line Detection , 1971, JACM.