Experimental Performance Characterization of Edge Detection in Computer Vision

随着新的边缘检测论文的出现，令人信服的证据比过去的技术真正的优势是社区认可和接受的关键。这需要建立客观和定量地证明新算法是否提供性能改进的方法。这项研究将产生一个精心开发和记录的实验框架，通过自适应采样边缘检测器参数空间和生成ROC曲线来评估边缘检测器。这项研究包括记录像素级评估的结果与基于更高级别任务（如感知分组、运动结构和人类物体识别）的评估的一致程度。这项工作将留下必要的人工制品（图像集，软件），供其他人使用，并以此为基础进行研究。特别是，将创建一个网站，以促进将此框架作为标准技术的使用。除了为边缘检测器的性能评估提供解决方案之外，研究结果也可以作为开发其他计算机视觉问题的性能评估方法的模型。

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.