基于距离数据的三维目标识别特征形状

Richard J. Campbell, P. Flynn
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引用次数: 67

摘要

最近在物体识别方面的许多研究都采用了基于外观的方案,其中待识别的物体被表示为由从训练视图中获得的许多特征向量(特征图像)所跨越的多维空间中的原型集合。在本文中,我们扩展了基于外观的识别方案来处理距离(形状)数据。训练的结果是一组捕捉物体大致形状的“特征面”。这些技术被用来形成一个识别任意旋转姿态变换下的物体的系统。该系统已在一个包含自由曲面对象的20个对象数据库和一个包含54个对象的成品零件数据库上进行了测试。系统的实验表明了该系统的优点,同时也指出了未来研究中需要研究的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eigenshapes for 3D object recognition in range data
Much of the recent research in object recognition has adopted an appearance-based scheme, wherein objects to be recognized are represented as a collection of prototypes in a multidimensional space spanned by a number of characteristic vectors (eigen-images) obtained from training views. In this paper, we extend the appearance-based recognition scheme to handle range (shape) data. The result of training is a set of 'eigensurfaces' that capture the gross shape of the objects. These techniques are used to form a system that recognizes objects under an arbitrary rotational pose transformation. The system has been tested on a 20 object database including free-form objects and a 54 object database of manufactured parts. Experiments with the system point out advantages and also highlight challenges that must be studied in future research.
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