Experimental Performance Characterization of Edge Detection in Computer Vision

计算机视觉中边缘检测的实验性能表征

• 批准号: 9731821
• 负责人: Kevin Bowyer
• 金额: $ 23.69万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-15 至 2002-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9731821&HistoricalAwards=false
• 关键词: Experimental; Performance; Characterization; Edge; Detection

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.

随着新的边缘检测论文的出现，令人信服地证明相对于过去技术的真正优势对于社区的认可和接受至关重要。 这就需要建立方法来客观、定量地证明新算法是否能够提供性能改进。 这项研究将通过自适应采样边缘检测器参数空间并生成 ROC 曲线，为边缘检测器评估提供精心开发和记录的实验框架。 该研究包括记录像素级评估的结果与基于更高级别任务（例如感知分组、运动结构和人体物体识别）的评估的一致程度。 这项工作将留下其他人使用和建立研究所需的工件（图像集、软件）。 特别是，将创建一个网站来促进使用该框架作为标准技术。 除了为边缘检测器的性能评估提供解决方案外，该结果还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。