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- W2145087262 abstract "Numerous interaction techniques have been developed that make virtual pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets." @default.
- W2145087262 created "2016-06-24" @default.
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- W2145087262 date "2012-02-14" @default.
- W2145087262 modified "2023-10-17" @default.
- W2145087262 title "Probabilistic pointing target prediction via inverse optimal control" @default.
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- W2145087262 doi "https://doi.org/10.1145/2166966.2166968" @default.
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