New approach of estimating edge detection threshold and application of adaptive detector depending on image complexity

Abstract

This paper presents a new approach to edge detection based on the application of an adequate adaptive algorithm for edge detection depending on the image complexity criteria. The complexity of the image was determined based on spatial information. Also, a new approach of estimating threshold for edge detection has been developed, which is based on the principles of machine learning, by applying grid search and random search. For analysis and testing, images with real scenes were used. This dataset was used to improve threshold estimation using the grid and random search methods. Based on the obtained results, it can be seen that the complexity significantly affects the edge detection, so with a low and medium number of details, the Roberts operator achieved good results. For images with a high number of details, the Canny operator has proven to be the best solution. New approach for estimate the threshold based on the grid search improves edge detection, especially with the Canny operator. Grid search-based method gives best results but it takes a lot of computational time. If computational time and the improvements obtained are taken into account, more effective optimization of the parameters for estimating threshold values is random search method.