Learning and Matching Line Aspects for Articulated Objects

Xiaofeng Ren
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引用次数: 19

Abstract

Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. Groundtruth segmentations in video sequences are utilized for both training and testing aspect models that operate on static images. We represent aspects of an articulated object as collections of line segments. In learning aspects, where object centers are known, a linear matching based on line location and orientation is used to measure similarity between views. We use K-medoid to find cluster centers. When using line aspects in recognition, matching is based on pairwise cues of relative location, relative orientation as well adjacency and parallelism. Matching with pairwise cues leads to a quadratic optimization that we solve with a spectral approximation. We show that our line aspect matching is capable of locating people in a variety of poses. Line aspect matching performs significantly better than an alternative approach using Hausdorff distance, showing merits of the line representation.
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铰接对象的线方面学习与匹配
传统的方面图是基于拓扑的,对于铰接对象是不切实际的。在这项工作中,我们从视频数据中学习了一小部分方面,或者说是原型视图。视频序列中的真值分割用于训练和测试在静态图像上操作的方面模型。我们将一个铰接对象的各个方面表示为线段的集合。在学习方面,当物体中心已知时,使用基于线位置和方向的线性匹配来度量视图之间的相似性。我们使用K-medoid来寻找聚类中心。在使用线方面进行识别时,匹配是基于相对位置、相对方向以及相邻性和平行性的成对线索。与成对线索的匹配导致我们用谱近似求解的二次优化。我们证明了我们的直线方面匹配能够定位各种姿势的人。直线方面匹配的性能明显优于使用豪斯多夫距离的替代方法,显示了直线表示的优点。
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