多分辨率ORKA：使用K-近似图的快速和分辨率无关的物体重建
Multiresolution ORKA: fast and resolution independent object reconstruction using a K-approximation graph
摘要：
目标识别与重建是当前许多领域的研究热点。在交通摄像头中检测行人或汽车或在地球物理勘探中跟踪地震波只是许多应用中的两个。最近，作者开发了一种新的方法-使用K-近似（ORKA）的对象重建-从给定的数据中提取这样的对象。在该方法中，使用特殊的对象模型，其中对象的移动和变形可以被控制以适应应用。目前形式的ORKA高度依赖于数据分辨率。一方面，物体的运动只能在取决于数据分辨率的网格上重建。另一方面，运行时间随着分辨率呈指数级增长。因此，数据的分辨率需要在小范围内，其中重建足够准确，但运行时间不太高。在这项工作中，我们提出了一个多分辨率的方法，我们结合ORKA与小波分解的数据。然后迭代重建对象，这大大减少了运行时间。此外，我们可以提高数据分辨率，使得运动重建不再依赖于原始网格。我们还简要介绍了原始ORKA算法。因此，不需要以前的工作知识。
英文摘要：
Object recognition and reconstruction is of great interest in many research fields. Detecting pedestrians or cars in traffic cameras or tracking seismic waves in geophysical exploration are only two of many applications. Recently, the authors developed a new method - Object reconstruction using K-approximation (ORKA) - to extract such objects out of given data. In this method a special object model is used where the movement and deformation of the object can be controlled to fit the application. ORKA in its current form is highly dependent on the data resolution. On the one hand, the movement of the object can only be reconstructed on a grid that depends on the data resolution. On the other hand, the runtime increases exponentially with the resolution. Hence, the resolution of the data needs to be in a small range where the reconstruction is accurate enough but the runtime is not too high. In this work, we present a multiresolution approach, where we combine ORKA with a wavelet decomposition of the data. The object is then reconstructed iteratively what drastically reduces the runtime. Moreover, we can increase the data resolution such that the movement reconstruction no longer depends on the original grid. We also give a brief introduction on the original ORKA algorithm. Hence, knowledge of the previous work is not required.
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