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@Article{JOMM06-msar,
author = {Greg Schmidt and Dennis G. Brown and Erik B. Tomlin and
J. Edward {Swan~II} and Yohan Baillot},
title = {Probabilistic Algorithms, Integration, and Empirical Evaluation
for Disambiguating Multiple Selections in Frustum-Based Pointing},
journal = {Journal of Multimedia},
month = {June},
volume = 1,
number = 3,
year = 2006,
pages = {1--12},
abstract = {
Selection is a fundamental user operation in 3D environments. These environments
often simulate or augment the real world, and a part of that simulation is the
ability to select objects for observation and manipulation. Many user interfaces
for these applications depend on six-degree-of-freedom tracking devices. Such
devices have limited accuracy and are susceptible to noise, giving an
imprecision that makes object selections difficult and hard to repeat. This
difficulty is amplified when the user's viewpoint is also tracked, meaning the
user must compensate for noise from both the head tracker and the pointing
device when performing object selection. Also, users may experience fatigue when
using handheld pointing devices for extended periods, creating error even if the
tracking technology were perfect.
This paper presents a pointing-based probabilistic selection algorithm that
addresses some of the ambiguities associated with tracking and user imprecision.
It performs multiple selections by considering a frustum along the user's
pointing direction and the hierarchical structure of the database. It assigns
probabilities that the user has selected particular objects using a set of
low-level 3D intersection-based selection techniques and the relationship of the
objects in a hierarchical database, and makes the final selection using one of
several weighting schemes. We performed several experiments to evaluate the
low-level selection techniques, tested several weighting schemes for the integration
algorithm, and we show that the algorithm is effective at disambiguating multiple
selections.
},
}