928 research outputs found
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
Geometry of River Networks II: Distributions of Component Size and Number
The structure of a river network may be seen as a discrete set of nested
sub-networks built out of individual stream segments. These network components
are assigned an integral stream order via a hierarchical and discrete ordering
method. Exponential relationships, known as Horton's laws, between stream order
and ensemble-averaged quantities pertaining to network components are observed.
We extend these observations to incorporate fluctuations and all higher moments
by developing functional relationships between distributions. The relationships
determined are drawn from a combination of theoretical analysis, analysis of
real river networks including the Mississippi, Amazon and Nile, and numerical
simulations on a model of directed, random networks. Underlying distributions
of stream segment lengths are identified as exponential. Combinations of these
distributions form single-humped distributions with exponential tails, the sums
of which are in turn shown to give power law distributions of stream lengths.
Distributions of basin area and stream segment frequency are also addressed.
The calculations identify a single length-scale as a measure of size
fluctuations in network components. This article is the second in a series of
three addressing the geometry of river networks.Comment: 16 pages, 13 figures, 4 tables, Revtex4, submitted to PR
Spectral Pattern Recognition by a Two-Layer Perceptron: Effects of Training Set Size
Pattern recognition in urban areas is one of the most challenging issues in
classifying satellite remote sensing data. Parametric pixel-by-pixel classification
algorithms tend to perform poorly in this context. This is because urban areas
comprise a complex spatial assemblage of disparate land cover types - including
built structures, numerous vegetation types, bare soil and water bodies. Thus,
there is a need for more powerful spectral pattern recognition techniques,
utilizing pixel-by-pixel spectral information as the basis for automated urban
land cover detection. This paper adopts the multi-layer perceptron classifier
suggested and implemented in [5]. The objective of this study is to analyse the
performance and stability of this classifier - trained and tested for supervised
classification (8 a priori given land use classes) of a Landsat-5 TM image
(270 x 360 pixels) from the city of Vienna and its northern surroundings
- along with varying the training data set in the single-training-site case.
The performance is measured in terms of total classification, map user's and
map producer's accuracies. In addition, the stability with initial parameter
conditions, classification error matrices, and error curves are analysed in some
detail. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc
Unified View of Scaling Laws for River Networks
Scaling laws that describe the structure of river networks are shown to
follow from three simple assumptions. These assumptions are: (1) river networks
are structurally self-similar, (2) single channels are self-affine, and (3)
overland flow into channels occurs over a characteristic distance (drainage
density is uniform). We obtain a complete set of scaling relations connecting
the exponents of these scaling laws and find that only two of these exponents
are independent. We further demonstrate that the two predominant descriptions
of network structure (Tokunaga's law and Horton's laws) are equivalent in the
case of landscapes with uniform drainage density. The results are tested with
data from both real landscapes and a special class of random networks.Comment: 14 pages, 9 figures, 4 tables (converted to Revtex4, PRE ref added
Geometry of River Networks I: Scaling, Fluctuations, and Deviations
This article is the first in a series of three papers investigating the
detailed geometry of river networks. Large-scale river networks mark an
important class of two-dimensional branching networks, being not only of
intrinsic interest but also a pervasive natural phenomenon. In the description
of river network structure, scaling laws are uniformly observed. Reported
values of scaling exponents vary suggesting that no unique set of scaling
exponents exists. To improve this current understanding of scaling in river
networks and to provide a fuller description of branching network structure, we
report here a theoretical and empirical study of fluctuations about and
deviations from scaling. We examine data for continent-scale river networks
such as the Mississippi and the Amazon and draw inspiration from a simple model
of directed, random networks. We center our investigations on the scaling of
the length of sub-basin's dominant stream with its area, a characterization of
basin shape known as Hack's law. We generalize this relationship to a joint
probability density and show that fluctuations about scaling are substantial.
We find strong deviations from scaling at small scales which can be explained
by the existence of linear network structure. At intermediate scales, we find
slow drifts in exponent values indicating that scaling is only approximately
obeyed and that universality remains indeterminate. At large scales, we observe
a breakdown in scaling due to decreasing sample space and correlations with
overall basin shape. The extent of approximate scaling is significantly
restricted by these deviations and will not be improved by increases in network
resolution.Comment: 16 pages, 13 figures, Revtex4, submitted to PR
Quantifying loopy network architectures
Biology presents many examples of planar distribution and structural networks
having dense sets of closed loops. An archetype of this form of network
organization is the vasculature of dicotyledonous leaves, which showcases a
hierarchically-nested architecture containing closed loops at many different
levels. Although a number of methods have been proposed to measure aspects of
the structure of such networks, a robust metric to quantify their hierarchical
organization is still lacking. We present an algorithmic framework, the
hierarchical loop decomposition, that allows mapping loopy networks to binary
trees, preserving in the connectivity of the trees the architecture of the
original graph. We apply this framework to investigate computer generated
graphs, such as artificial models and optimal distribution networks, as well as
natural graphs extracted from digitized images of dicotyledonous leaves and
vasculature of rat cerebral neocortex. We calculate various metrics based on
the Asymmetry, the cumulative size distribution and the Strahler bifurcation
ratios of the corresponding trees and discuss the relationship of these
quantities to the architectural organization of the original graphs. This
algorithmic framework decouples the geometric information (exact location of
edges and nodes) from the metric topology (connectivity and edge weight) and it
ultimately allows us to perform a quantitative statistical comparison between
predictions of theoretical models and naturally occurring loopy graphs.Comment: 17 pages, 8 figures. During preparation of this manuscript the
authors became aware of the work of Mileyko at al., concurrently submitted
for publicatio
Runoff Estimation and Morphometric Analysis for Hesaraghatta Watershed Using IRS–1D LISS III FCC Satellite Data
Tools for Semi-automated Landform Classification: A Comparison in the Basilicata Region (Southern Italy)
Recent advances in spatial methods of digital elevation model
(DEMs) analysis have addressed many research topics on the assessment of
morphometric parameters of the landscape. Development of computer algorithms for calculating the geomorphometric properties of the Earth’s surface has
allowed for expanding of some methods in the semi-automatic recognition and
classification of landscape features. In such a way, several papers have been
produced, documenting the applicability of the landform classification based on
map algebra. The Topographic Position Index (TPI) is one of the most widely
used parameters for semi-automated landform classification using GIS software.
The aim was to apply the TPI classes for landform classification in the Basilicata
Region (Southern Italy). The Basilicata Region is characterized by an extremely
heterogeneous landscape and geological features. The automated landform
extraction, starting from two different resolution DEMs at 20 and 5 m-grids, has
been carried out by using three different GIS software: Arcview, Arcmap, and
SAGA. Comparison of the landform maps resulting from each software at a
different scale has been realized, furnishing at the end the best landform map and
consequently a discussion over which is the best software implementation of the
TPI method
Usability of VGI for validation of land cover maps
Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation
Lateral Distribution of Muons in IceCube Cosmic Ray Events
In cosmic ray air showers, the muon lateral separation from the center of the
shower is a measure of the transverse momentum that the muon parent acquired in
the cosmic ray interaction. IceCube has observed cosmic ray interactions that
produce muons laterally separated by up to 400 m from the shower core, a factor
of 6 larger distance than previous measurements. These muons originate in high
pT (> 2 GeV/c) interactions from the incident cosmic ray, or high-energy
secondary interactions. The separation distribution shows a transition to a
power law at large values, indicating the presence of a hard pT component that
can be described by perturbative quantum chromodynamics. However, the rates and
the zenith angle distributions of these events are not well reproduced with the
cosmic ray models tested here, even those that include charm interactions. This
discrepancy may be explained by a larger fraction of kaons and charmed
particles than is currently incorporated in the simulations
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