20,456 research outputs found
DPPD: Deformable Polar Polygon Object Detection
Regular object detection methods output rectangle bounding boxes, which are
unable to accurately describe the actual object shapes. Instance segmentation
methods output pixel-level labels, which are computationally expensive for
real-time applications. Therefore, a polygon representation is needed to
achieve precise shape alignment, while retaining low computation cost. We
develop a novel Deformable Polar Polygon Object Detection method (DPPD) to
detect objects in polygon shapes. In particular, our network predicts, for each
object, a sparse set of flexible vertices to construct the polygon, where each
vertex is represented by a pair of angle and distance in the Polar coordinate
system. To enable training, both ground truth and predicted polygons are
densely resampled to have the same number of vertices with equal-spaced
raypoints. The resampling operation is fully differentable, allowing gradient
back-propagation. Sparse polygon predicton ensures high-speed runtime inference
while dense resampling allows the network to learn object shapes with high
precision. The polygon detection head is established on top of an anchor-free
and NMS-free network architecture. DPPD has been demonstrated successfully in
various object detection tasks for autonomous driving such as traffic-sign,
crosswalk, vehicle and pedestrian objects
Partitioning Regular Polygons into Circular Pieces I: Convex Partitions
We explore an instance of the question of partitioning a polygon into pieces,
each of which is as ``circular'' as possible, in the sense of having an aspect
ratio close to 1. The aspect ratio of a polygon is the ratio of the diameters
of the smallest circumscribing circle to the largest inscribed disk. The
problem is rich even for partitioning regular polygons into convex pieces, the
focus of this paper. We show that the optimal (most circular) partition for an
equilateral triangle has an infinite number of pieces, with the lower bound
approachable to any accuracy desired by a particular finite partition. For
pentagons and all regular k-gons, k > 5, the unpartitioned polygon is already
optimal. The square presents an interesting intermediate case. Here the
one-piece partition is not optimal, but nor is the trivial lower bound
approachable. We narrow the optimal ratio to an aspect-ratio gap of 0.01082
with several somewhat intricate partitions.Comment: 21 pages, 25 figure
Separation-Sensitive Collision Detection for Convex Objects
We develop a class of new kinetic data structures for collision detection
between moving convex polytopes; the performance of these structures is
sensitive to the separation of the polytopes during their motion. For two
convex polygons in the plane, let be the maximum diameter of the polygons,
and let be the minimum distance between them during their motion. Our
separation certificate changes times when the relative motion of
the two polygons is a translation along a straight line or convex curve,
for translation along an algebraic trajectory, and for
algebraic rigid motion (translation and rotation). Each certificate update is
performed in time. Variants of these data structures are also
shown that exhibit \emph{hysteresis}---after a separation certificate fails,
the new certificate cannot fail again until the objects have moved by some
constant fraction of their current separation. We can then bound the number of
events by the combinatorial size of a certain cover of the motion path by
balls.Comment: 10 pages, 8 figures; to appear in Proc. 10th Annual ACM-SIAM
Symposium on Discrete Algorithms, 1999; see also
http://www.uiuc.edu/ph/www/jeffe/pubs/kollide.html ; v2 replaces submission
with camera-ready versio
K-coverage in regular deterministic sensor deployments
An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k â„ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments
An n-sided polygonal model to calculate the impact of cyber security events
This paper presents a model to represent graphically the impact of cyber
events (e.g., attacks, countermeasures) in a polygonal systems of n-sides. The
approach considers information about all entities composing an information
system (e.g., users, IP addresses, communication protocols, physical and
logical resources, etc.). Every axis is composed of entities that contribute to
the execution of the security event. Each entity has an associated weighting
factor that measures its contribution using a multi-criteria methodology named
CARVER. The graphical representation of cyber events is depicted as straight
lines (one dimension) or polygons (two or more dimensions). Geometrical
operations are used to compute the size (i.e, length, perimeter, surface area)
and thus the impact of each event. As a result, it is possible to identify and
compare the magnitude of cyber events. A case study with multiple security
events is presented as an illustration on how the model is built and computed.Comment: 16 pages, 5 figures, 2 tables, 11th International Conference on Risks
and Security of Internet and Systems, (CRiSIS 2016), Roscoff, France,
September 201
Polygonal Building Segmentation by Frame Field Learning
While state of the art image segmentation models typically output
segmentations in raster format, applications in geographic information systems
often require vector polygons. To help bridge the gap between deep network
output and the format used in downstream tasks, we add a frame field output to
a deep segmentation model for extracting buildings from remote sensing images.
We train a deep neural network that aligns a predicted frame field to ground
truth contours. This additional objective improves segmentation quality by
leveraging multi-task learning and provides structural information that later
facilitates polygonization; we also introduce a polygonization algorithm that
utilizes the frame field along with the raster segmentation. Our code is
available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.Comment: CVPR 2021 - IEEE Conference on Computer Vision and Pattern
Recognition, Jun 2021, Pittsburg / Virtual, United State
The VOISE Algorithm: a Versatile Tool for Automatic Segmentation of Astronomical Images
The auroras on Jupiter and Saturn can be studied with a high sensitivity and
resolution by the Hubble Space Telescope (HST) ultraviolet (UV) and
far-ultraviolet (FUV) Space Telescope spectrograph (STIS) and Advanced Camera
for Surveys (ACS) instruments. We present results of automatic detection and
segmentation of Jupiter's auroral emissions as observed by HST ACS instrument
with VOronoi Image SEgmentation (VOISE). VOISE is a dynamic algorithm for
partitioning the underlying pixel grid of an image into regions according to a
prescribed homogeneity criterion. The algorithm consists of an iterative
procedure that dynamically constructs a tessellation of the image plane based
on a Voronoi Diagram, until the intensity of the underlying image within each
region is classified as homogeneous. The computed tessellations allow the
extraction of quantitative information about the auroral features such as mean
intensity, latitudinal and longitudinal extents and length scales. These
outputs thus represent a more automated and objective method of characterising
auroral emissions than manual inspection.Comment: 9 pages, 7 figures; accepted for publication in MNRA
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