On Trajectory Representation for Scientific Features
On Trajectory Representation for Scientific Features
复制标题
科学特征的轨迹表示
DOI：
10.1109/icdm.2006.120
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
R. Machiraju
中科院分区：
文献类型：
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作者：
S. Mehta;S. Parthasarathy;R. Machiraju
在本文中，我们提出了在时间变化的科学数据集中发现的有形特征的轨迹表示算法。我们没有将特征建模为点，而是考虑了特征的形状和范围等属性。我们的论点是，这些属性在理解时间演化和特征之间的相互作用方面起着重要作用。该方法基于运动和形状参数，包括线速度、角速度等。我们使用这些参数来分割轨迹，而不是依赖于轨迹的几何形状。我们在来自不同领域的真实数据集上评估了我们的算法。我们通过高精度地重建轨迹来证明运动和形状参数估计的准确性。最后，我们给出了性能和可伸缩性结果。
In this article, we present trajectory representation algorithms for tangible features found in temporally varying scientific datasets. Rather than modeling the features as points, we take attributes like shape and extent of the feature into account. Our contention is that these attributes play an important role in understanding the temporal evolution and interactions among features. The proposed representation scheme is based on motion and shape parameters including linear velocity, angular velocity, etc. We use these parameters to segment the trajectory instead of relying on the geometry of the trajectory. We evaluate our algorithms on real datasets originating from different domains. We show the accuracy of the motion and shape parameter estimation by reconstructing the trajectories with high accuracy. Finally, we present performance and scalability results.