Exact(8)
Some special properties of wrap-around L2-discrepancy can be used to make our algorithm much more efficient.
To solve this problem and make our algorithm robust under multi-camera setting, we take full advantage of low-level features, attributes and inter-attribute correlations at the same time.
We are also inspired and motivated to improve techniques to make our algorithm "Rank-Sort-MDP(_{mathrm {REF}}) algorithm" faster and faster so as to be able to work on big databases, the processing of which necessitates less time-consuming techniques.
We also plan to make our algorithm more widely usable, for instance through a CellDesigner [32] XPP-import plugin.
This will make our algorithm deterministic since the eigenvector directions are always unique.
If an algorithm for matrix multiplication in time O(n) is found this would make our algorithm run in time O(n2.5).
As explained in Methods, in order to make this observation more quantitative we estimate an optimal line which discriminates between solenoid and non-solenoid sequences that can be used to make our algorithm predictive.
Taking the adult VNC as an example, we can make our algorithm more robust by first optimizing the skeleton of the main trunk (i.e. the horizontal domain in Fig. 8a) so that we can get a good estimation of the scale, position and the orientation of the VNC, and then optimizing the remaining domains/segments to complete the entire skeleton.
Related(20)
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