Department of Computer Science, RyersonUniversity, 350 Victoria Street, Toronto, ON, Canada M5B 2K3.
IEEE Trans Pattern Anal Mach Intell. 2010 Jul;32(7):1317-24. doi: 10.1109/TPAMI.2009.146.
Fiducial markers are artificial landmarks added to a scene to facilitate locating point correspondences between images, or between images and a known model. Reliable fiducials solve the interest point detection and matching problems when adding markers is convenient. The proper design of fiducials and the associated computer vision algorithms to detect them can enable accurate pose detection for applications ranging from augmented reality, input devices for HCI, to robot navigation. Marker systems typically have two stages, hypothesis generation from unique image features and verification/identification. A set of criteria for high robustness and practical use are identified and then optimized to produce the ARTag fiducial marker system. An edge-based method robust to lighting and partial occlusion is used for the hypothesis stage, and a reliable digital coding system is used for the identification and verification stage. Using these design criteria large gains in performance are achieved by ARTag over conventional ad hoc designs.
基准标记是添加到场景中的人工地标，以方便在图像之间或图像与已知模型之间找到点对应关系。当添加标记方便时，可靠的基准标记可以解决兴趣点检测和匹配问题。适当的基准标记设计和相关的计算机视觉算法来检测它们可以为从增强现实、人机交互输入设备到机器人导航等各种应用程序实现准确的姿势检测。标记系统通常有两个阶段，从独特的图像特征生成假设和验证/识别。然后确定一组用于高鲁棒性和实际使用的标准，并进行优化以生成 ARTag 基准标记系统。假设阶段使用对光照和部分遮挡具有鲁棒性的基于边缘的方法，而识别和验证阶段则使用可靠的数字编码系统。使用这些设计标准，ARTag 在性能上相对于传统的特定设计有了很大的提高。
