Appearance Manifold with Embedded Covariance Matrix for Robust 3D Object Recognition
Appearance Manifold with Embedded Covariance Matrix for Robust 3D Object Recognition
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具有嵌入式协方差矩阵的外观流形，用于稳健的 3D 对象识别
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
H. Murase
中科院分区：
文献类型：
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作者：
Lina;Tomokazu Takahashi;I. Ide;H. Murase
我们建议使用嵌入协方差矩阵的外观流形作为一种技术，用于从受几何和质量退化影响的图像中识别3D对象。我们的策略涵盖了这个外观流形的建设，考虑到构成的变化。在该方法中，每个学习姿势的对应关系不是基于特征点，而是直接从协方差矩阵。因此，我们消除了依赖于特征点到特征点的对应关系，这是由于特征点的移动位置的现象的误分类的主要原因。实验结果表明，我们的方法实现了更高的识别精度比使用一个简单的外观流形。因此，它可以提供一种更有效的方式来开发一个强大的3D对象识别系统。
We propose use of an appearance manifold with embedded covariance matrix as a technique for recognizing 3D objects from images that are influenced by geometric and quality-degraded effects. Our strategy covers the construction of this appearance manifold by giving consideration to pose changes. In the proposed method, the correspondence of each learning pose is not based on the eigenpoint but directly from the covariance matrix. Thus, we eliminate the dependency on eigenpoint-to-eigenpoint correspondence, which is the main cause of misclassification due to the phenomenon of the eigenpoint’s shifting position. Experimental results show that our approach achieves higher recognition accuracies than using a simple appearance manifold. Consequently, it can provide a more efficient way of developing a robust 3D object recognition system.