Robust Object Recognition Based on Regular Framing and Depth Aspect Image
Robust Object Recognition Based on Regular Framing and Depth Aspect Image
复制标题
基于规则分幅和深度图像的鲁棒目标识别
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
S. Kaneko
中科院分区：
文献类型：
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作者：
T. Takeguchi;S. Kaneko
提出了一种针对包含多个目标的复杂深度场景的基于模型的目标识别方法。一种新的模型表示，命名为深度方面的图像，也被定义为一个方向标准化的外观，从原始的深度数据的对象，这是通过元组的三个重心定义在定期定义的体素。一个鲁棒的匹配方案，命名为残差的最小分位数，不仅可以实现目标识别的深度方面的图像，但与候选模型的验证。稀疏分布的质心和鲁棒的匹配使得基于ICP的粗配准和随后的验证过程更加快速和可靠。在本文中，我们展示了识别实验100个场景包含多个对象从4个模型库。
A method of model-based object recognition for a cluttered depth scene including multiple objects is proposed. A novel model representation, named depth aspect image, is also defined as an orientation standardized appearance from the original depth data of objects, which is transformed through tuples of three barycenters defined within regularly defined voxels. A robust matching scheme, named least quantile of residuals, can achieve not only object recognition with depth aspect images but also verification with candidate models. The sparsely distributed barycenters and the robust matching make the ICP-based rough registration and the following verification process much faster and more reliable. In this paper, we show recognition experiments on 100 scenes contained multiple objects from a library of 4 models.