As new edge detection papers appear, convincing proof of real advantages over past techniques is critical to community recognition and acceptance. This requires establishment of methods to objectively and quantitatively demonstrate whether a new algorithm offers performance improvements. This research will result in a carefully developed and documented experimental framework for edge detector evaluation by adaptively sampling edge detector parameter space and generating ROC curves. The research includes documenting how well the results of the pixel-level evaluation agree with evaluations based on higher-level tasks such as perceptual grouping, structure from motion and human object recognition. The work will leave behind the artifacts (image sets, software, ) necessary for others to use, and to build upon, the research. In particular, a web site will be created that promotes the use of this framework as a standard technique. Beyond providing a solution for performance evaluation of edge detectors, the results may also serve as a model for the development of performance evaluation methods for other computer vision problems.
