Evaluation of edge detectors using average risk

A new method for evaluation of edge detectors, based on the average risk of a decision, is discussed. The average risk is a performance measure well-known in Bayesian decision theory. Since edge detection can be regarded as a compound decision making process, the performance of an edge detector is context dependent. Therefore, the application of average risk to edge detection is non-trivial. The paper describes a method to estimate the probabilities on a number of different types of (context dependent) errors. A weighted sum of these estimated probabilities represents the average risk. The weight coefficients define the cost function. The method is suitable, not only for the comparison of edge operators, but also for the determination of the weaknesses and strengths of a certain edge operator. This is demonstrated with some well-known edge operators.< >