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This would make our method likely overestimate the actual number of GSR events.
Further, to make our method be implemented easily in CAD/CAM systems, a specific implementation process is also briefly presented.
Actually we are working to make our method as more automatic as possible, to select the best starting pixel to draw the contour.
In this paper, we use a network to mimic this process, which has an inherent attribute to make our method get better performance.
The building cube method is adopted to make our method suitable for massive parallelization systems, which can reduce the calculation and turnaround times immensely.
The scalability with large motion dataset, and insensitivity with the algorithm parameters, make our method can be widely used in real-world applications.
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Finally, in order to make our methods more accessible to non-programmers, we provide a Cytoscape interface to the R implementation as a plugin, CytoCopteR.
Importantly, our approach does not require any priori knowledge, which makes our method more practical in medical decision making.
This makes our method more efficient and faster.
This makes our method of great interest as compared with traditional time-consuming Monte Carlo simulations.
This makes our method well suited for reverse engineering products made of leather, bent cardboard or metal sheets.
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