Objective comparison of edge detection assessment methods based on genetic optimization

Abstract

For many image processing applications, edge detection is a very important task that needs to be assessed, since the success or failure of these applications depends on the performance of this task. Assessment of edge detection is largely subjective; however, current trends in the image processing community are moving toward objective assessment. In recent years, many different methods have been proposed to assess edge detection, although no agreement has been reached as the proper method, since previous comparisons have produced contrasting results. A comparison of assessment methods using an objective approach is presented. Methods are compared by analyzing the results of an optimization procedure using genetic algorithms and the methods as fitness. The comparison is based on the premise that better assessment methods will lead the optimization procedure to produce better results. A cross-validation is carried out to compare the results obtained using one assessment method with others. Conclusions provide recommendations for authors interested in assessing edge detection algorithms.