Visual Media Lab, Ben-Gurion University, Israel.
IEEE Trans Vis Comput Graph. 2011 Oct;17(10):1369-79. doi: 10.1109/TVCG.2010.241.
Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class library. Nestor performs shape recognition by analyzing contour structures and generating projective-invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone.
Nestor 是一个实时识别和相机位姿估计系统，用于平面形状。该系统允许将对人类具有上下文意义的形状用作增强现实 (AR) 跟踪目标。用户可以实时向系统教授新的形状。新的形状可以正面显示给系统，也可以根据以前学习的形状自动校正。可以根据形状类别库对形状进行分类，自动为其分配虚拟内容。Nestor 通过分析轮廓结构并从其凹部生成投影不变签名来执行形状识别。凹部进一步用于提取用于姿态估计和跟踪的特征。通过最小化每个图像轮廓及其库对应物上的样本点之间的重投影误差来进行姿态细化。通过实时演化活动轮廓来匹配样本点。我们的实验表明，该系统提供了稳定而准确的配准，并在诺基亚 N95 手机上以交互帧率运行。
