Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy

Abstract

This paper proposes a novel Canny algorithm without manually setting parameters. Adaptive filter design, implementation and automatic calculation of low and high thresholds are studied in this paper. A general auto-regressive model is deduced that uses only uniform expression for both the linear and non-linear autoregressive model based on Weierstrass theory. Moreover, the bi-dimensional expression of the model is deduced by using bi-vectors instead of scalar parameters. The Generalized M-estimator is chosen for the new model. An adaptive filter is implemented based on the general auto-regression model and simulations are carried out. Gray entropy mathematical model is established according to the gray level-gradient co-occurrence matrix of image and the simulated annealing algorithm is used to solve the gray entropy model. Experiments are done on the worldwide datasets to evaluate the performance of our method. Results demonstrate the superiority of our method compared with the best parameter values method and standard Canny, especially when images are polluted by mixed noises containing Gaussian noise, Poisson noise and impulse noise.