12,790 research outputs found
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
Automated Quantitative Description of Spiral Galaxy Arm-Segment Structure
We describe a system for the automatic quantification of structure in spiral
galaxies. This enables translation of sky survey images into data needed to
help address fundamental astrophysical questions such as the origin of spiral
structure---a phenomenon that has eluded theoretical description despite 150
years of study (Sellwood 2010). The difficulty of automated measurement is
underscored by the fact that, to date, only manual efforts (such as the citizen
science project Galaxy Zoo) have been able to extract information about large
samples of spiral galaxies. An automated approach will be needed to eliminate
measurement subjectivity and handle the otherwise-overwhelming image quantities
(up to billions of images) from near-future surveys. Our approach automatically
describes spiral galaxy structure as a set of arcs, precisely describing spiral
arm segment arrangement while retaining the flexibility needed to accommodate
the observed wide variety of spiral galaxy structure. The largest existing
quantitative measurements were manually-guided and encompassed fewer than 100
galaxies, while we have already applied our method to more than 29,000
galaxies. Our output matches previous information, both quantitatively over
small existing samples, and qualitatively against human classifications from
Galaxy Zoo.Comment: 9 pages;4 figures; 2 tables; accepted to CVPR (Computer Vision and
Pattern Recognition), June 2012, Providence, Rhode Island, June 16-21, 201
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
This paper focuses on multi-scale approaches for variational methods and
corresponding gradient flows. Recently, for convex regularization functionals
such as total variation, new theory and algorithms for nonlinear eigenvalue
problems via nonlinear spectral decompositions have been developed. Those
methods open new directions for advanced image filtering. However, for an
effective use in image segmentation and shape decomposition, a clear
interpretation of the spectral response regarding size and intensity scales is
needed but lacking in current approaches. In this context, data
fidelities are particularly helpful due to their interesting multi-scale
properties such as contrast invariance. Hence, the novelty of this work is the
combination of -based multi-scale methods with nonlinear spectral
decompositions. We compare with scale-space methods in view of
spectral image representation and decomposition. We show that the contrast
invariant multi-scale behavior of promotes sparsity in the spectral
response providing more informative decompositions. We provide a numerical
method and analyze synthetic and biomedical images at which decomposition leads
to improved segmentation.Comment: 13 pages, 7 figures, conference SSVM 201
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
Evidence for Filamentarity in the Las Campanas Redshift Survey
We apply Shapefinders, statistical measures of `shape' constructed from two
dimensional partial Minkowski functionals, to study the degree of filamentarity
in the Las Campanas Redshift Survey (LCRS). In two dimensions, three Minkowski
functionals characterise the morphology of an object, they are: its perimeter
(L), area (S), and genus. Out of L and S a single dimensionless Shapefinder
Statistic, F can be constructed (0 <=F <=1). F acquires extreme values on a
circle (F = 0) and a filament (F = 1). Using F, we quantify the extent of
filamentarity in the LCRS by comparing our results with a Poisson distribution
with similar geometrical properties and having the same selection function as
the survey. Our results unambiguously demonstrate that the LCRS displays a high
degree of filamentarity both in the Northern and Southern galactic sections a
result that is in general agreement with the visual appearance of the
catalogue. It is well known that gravitational clustering from Gaussian initial
conditions gives rise to the development of non-Gaussianity reflected in the
formation of a network-like filamentary structure on supercluster scales.
Consequently the fact that the smoothed LCRS catalogue shows properties
consistent with those of a Gaussian random field (Colley 1997) whereas the
unsmoothed catalogue demonstrates the presence of filamentarity lends strong
support to the conjecture that the large scale clustering of galaxies is driven
by gravitational instability.Comment: Accepted for publication in Ap
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