411,609 research outputs found
Sweeping an oval to a vanishing point
Given a convex region in the plane, and a sweep-line as a tool, what is best
way to reduce the region to a single point by a sequence of sweeps? The problem
of sweeping points by orthogonal sweeps was first studied in [2]. Here we
consider the following \emph{slanted} variant of sweeping recently introduced
in [1]: In a single sweep, the sweep-line is placed at a start position
somewhere in the plane, then moved continuously according to a sweep vector
(not necessarily orthogonal to the sweep-line) to another parallel end
position, and then lifted from the plane. The cost of a sequence of sweeps is
the sum of the lengths of the sweep vectors. The (optimal) sweeping cost of a
region is the infimum of the costs over all finite sweeping sequences for that
region. An optimal sweeping sequence for a region is one with a minimum total
cost, if it exists. Another parameter of interest is the number of sweeps.
We show that there exist convex regions for which the optimal sweeping cost
cannot be attained by two sweeps. This disproves a conjecture of Bousany,
Karker, O'Rourke, and Sparaco stating that two sweeps (with vectors along the
two adjacent sides of a minimum-perimeter enclosing parallelogram) always
suffice [1]. Moreover, we conjecture that for some convex regions, no finite
sweeping sequence is optimal. On the other hand, we show that both the 2-sweep
algorithm based on minimum-perimeter enclosing rectangle and the 2-sweep
algorithm based on minimum-perimeter enclosing parallelogram achieve a approximation in this sweeping model.Comment: 9 pages, 4 figure
Fixation prediction with a combined model of bottom-up saliency and vanishing point
By predicting where humans look in natural scenes, we can understand how they
perceive complex natural scenes and prioritize information for further
high-level visual processing. Several models have been proposed for this
purpose, yet there is a gap between best existing saliency models and human
performance. While many researchers have developed purely computational models
for fixation prediction, less attempts have been made to discover cognitive
factors that guide gaze. Here, we study the effect of a particular type of
scene structural information, known as the vanishing point, and show that human
gaze is attracted to the vanishing point regions. We record eye movements of 10
observers over 532 images, out of which 319 have vanishing points. We then
construct a combined model of traditional saliency and a vanishing point
channel and show that our model outperforms state of the art saliency models
using three scores on our dataset.Comment: arXiv admin note: text overlap with arXiv:1512.0172
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