Optimal parameters for edge detection

Bennamoum, and Masoud, Bennamoum and Bayoumi (1991), suggested a robust edge detector which relaxes the trade-off between robustness against noise and accurate localization of the edges. This hybrid detector separates the tasks of localization and noise suppression between two sub-detectors. In this paper, we present an extension to this hybrid detector to determine its optimal parameters, independently of the scene. This extension defines a probabilistic cost function using for criteria the probability of missing an edge buried in noise and the probability of detecting false edges. The optimization of this cost function allows the automatic selection of the parameters of the hybrid edge detector given the height of the minimum edge to be detected and the variance of the noise, σ_n². The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors