14,188 research outputs found
Consistent labeling of rotating maps
Dynamic maps that allow continuous map rotations, for example, on mobile
devices, encounter new geometric labeling issues unseen in static maps before. We study the
following dynamic map labeling problem: The input is an abstract map consisting of a set P
of points in the plane with attached horizontally aligned rectangular labels. While the map
with the point set P is rotated, all labels remain horizontally aligned. We are interested in
a consistent labeling of P under rotation, i.e., an assignment of a single (possibly empty)
active interval of angles for each label that determines its visibility under rotations such
that visible labels neither intersect each other (soft conflicts) nor occlude points in P at any
rotation angle (hard conflicts). Our goal is to find a consistent labeling that maximizes the
number of visible labels integrated over all rotation angles.
We first introduce a general model for labeling rotating maps and derive basic geometric
properties of consistent solutions. We show NP-hardness of the above optimization
problem even for unit-square labels. We then present a constant-factor approximation for
this problem based on line stabbing, and refine it further into an efficient polynomial-time
approximation scheme (EPTAS)
Labeling Points of Interest in Dynamic Maps using Disk Labels
Dynamic maps which support panning, rotating and zooming are available on every smartphone today. To label geographic features on these maps such that the user is presented with a consistent map view even on map interaction is a challenge. We are presenting a map labeling scheme, which allows to label maps at an interactive speed. For any possible map rotation the computed labeling remains free of intersections between labels. It is not required to remove labels from the map view to ensure this. The labeling scheme supports map panning and continuous zooming. During zooming a label appears and disappears only once. When zooming out of the map a label disappears only if it may overlap an equally or more important label in an arbitrary map rotation. This guarantees that more important labels are preferred to less important labels on small scale maps. We are presenting some extensions to the labeling that could be used for more sophisticated labeling features such as area labels turning into point labels at smaller map scales.
The proposed labeling scheme relies on a preprocessing phase. In this phase for each label the map scale where it is removed from the map view is computed. During the phase of map presentation the precomputed label set must only be filtered, what can be done very fast. We are presenting some hints that allow to efficiently compute the labeling in the preprocessing phase. Using these a labeling of about 11 million labels can be computed in less than 20 minutes. We are also presenting a datastructure to efficiently filter the precomputed label set in the interaction phase
Evaluation of Labeling Strategies for Rotating Maps
We consider the following problem of labeling points in a dynamic map that
allows rotation. We are given a set of points in the plane labeled by a set of
mutually disjoint labels, where each label is an axis-aligned rectangle
attached with one corner to its respective point. We require that each label
remains horizontally aligned during the map rotation and our goal is to find a
set of mutually non-overlapping active labels for every rotation angle so that the number of active labels over a full map rotation of
2 is maximized. We discuss and experimentally evaluate several labeling
models that define additional consistency constraints on label activities in
order to reduce flickering effects during monotone map rotation. We introduce
three heuristic algorithms and compare them experimentally to an existing
approximation algorithm and exact solutions obtained from an integer linear
program. Our results show that on the one hand low flickering can be achieved
at the expense of only a small reduction in the objective value, and that on
the other hand the proposed heuristics achieve a high labeling quality
significantly faster than the other methods.Comment: 16 pages, extended version of a SEA 2014 pape
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
In remote sensing images, the absolute orientation of objects is arbitrary.
Depending on an object's orientation and on a sensor's flight path, objects of
the same semantic class can be observed in different orientations in the same
image. Equivariance to rotation, in this context understood as responding with
a rotated semantic label map when subject to a rotation of the input image, is
therefore a very desirable feature, in particular for high capacity models,
such as Convolutional Neural Networks (CNNs). If rotation equivariance is
encoded in the network, the model is confronted with a simpler task and does
not need to learn specific (and redundant) weights to address rotated versions
of the same object class. In this work we propose a CNN architecture called
Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation
equivariance in the network itself. By using rotating convolutions as building
blocks and passing only the the values corresponding to the maximally
activating orientation throughout the network in the form of orientation
encoding vector fields, RotEqNet treats rotated versions of the same object
with the same filter bank and therefore achieves state-of-the-art performances
even when using very small architectures trained from scratch. We test RotEqNet
in two challenging sub-decimeter resolution semantic labeling problems, and
show that we can perform better than a standard CNN while requiring one order
of magnitude less parameters
Investigation of the neurovascular coupling in positive and negative BOLD responses in human brain at 7T
Decreases in stimulus-dependent blood oxygenation level dependent (BOLD) signal and their underlying neurovascular origins have recently gained considerable interest. In this study a multi-echo, BOLD-corrected vascular space occupancy (VASO) functional magnetic resonance imaging (fMRI) technique was used to investigate neurovascular responses during stimuli that elicit positive and negative BOLD responses in human brain at 7 T. Stimulus-induced BOLD, cerebral blood volume (CBV), and cerebral blood flow (CBF) changes were measured and analyzed in ‘arterial’ and ‘venous’ blood compartments in macro- and microvasculature. We found that the overall interplay of mean CBV, CBF and BOLD responses is similar for tasks inducing positive and negative BOLD responses. Some aspects of the neurovascular coupling however, such as the temporal response, cortical depth dependence, and the weighting between ‘arterial’ and ‘venous’ contributions, are significantly different for the different task conditions. Namely, while for excitatory tasks the BOLD response peaks at the cortical surface, and the CBV change is similar in cortex and pial vasculature, inhibitory tasks are associated with a maximum negative BOLD response in deeper layers, with CBV showing strong constriction of surface arteries and a faster return to baseline. The different interplays of CBV, CBF and BOLD during excitatory and inhibitory responses suggests different underlying hemodynamic mechanisms
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