首页> 外文会议>International symposium on advances in visual computing;ISVC 2009 >A Generalization of Moment Invariants on 2D Vector Fields to Tensor Fields of Arbitrary Order and Dimension
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A Generalization of Moment Invariants on 2D Vector Fields to Tensor Fields of Arbitrary Order and Dimension

机译:二维向量场上矩不变性到任意阶次和维数张量场的推广

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For object recognition in tensorfields and in pointclouds, the recognition of features in the object irrespective of their rotation is an important task. Rotationally invariant features exist for 2d scalar fields and for 3d scalar fields as moments of a second order structure tensor. For higher order structure tensors iterative algorithms for computing something similar to an eigenvector-decomposition exist. In this paper, we introduce a method to compute a basis for analytical rotationally invariant moments of tensorfields of - in principle - any order and dimension and give an example using up to 4th-order structure tensors in 3d.
机译:对于张量场和点云中的对象识别,不管对象的旋转如何,识别对象中的特征都是一项重要的任务。旋转不变特征存在于2d标量场和3d标量场中,作为二阶结构张量的矩。对于高阶结构张量,存在用于计算类似于特征向量分解的东西的迭代算法。在本文中,我们介绍了一种计算张量场的旋转不变矩的方法,该张量场在理论上是任意阶次和任意维度,并给出了在3d中使用最多四阶结构张量的示例。

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