9,242 research outputs found
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
Map-Based Navigation in a Graphical MOO
Traditional MUDs and MOOs lack support for global wareness and simple navigation. These problems can be addressed by the introduction of a map-based navigation tool. In this paper we report on the design and evaluation of such a tool for MOOsburg, a graphical 2D MOO based on the town of Blacksburg, Virginia. The tool supports exploration and place-based tasks in the MOO. It also allows navigation of a large-scale map and encourages users to develop survey knowledge of the town. An evaluation revealed some initial usability problems with our prototype and suggested new design ideas that may better support users. Using these results, the lessons learned about map-based navigation are presented
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
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