3,030 research outputs found
Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition
Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi
Deep Metric Learning via Lifted Structured Feature Embedding
Learning the distance metric between pairs of examples is of great importance
for learning and visual recognition. With the remarkable success from the state
of the art convolutional neural networks, recent works have shown promising
results on discriminatively training the networks to learn semantic feature
embeddings where similar examples are mapped close to each other and dissimilar
examples are mapped farther apart. In this paper, we describe an algorithm for
taking full advantage of the training batches in the neural network training by
lifting the vector of pairwise distances within the batch to the matrix of
pairwise distances. This step enables the algorithm to learn the state of the
art feature embedding by optimizing a novel structured prediction objective on
the lifted problem. Additionally, we collected Online Products dataset: 120k
images of 23k classes of online products for metric learning. Our experiments
on the CUB-200-2011, CARS196, and Online Products datasets demonstrate
significant improvement over existing deep feature embedding methods on all
experimented embedding sizes with the GoogLeNet network.Comment: 11 page
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
Algebraic error analysis of collinear feature points for camera parameter estimation
Cataloged from PDF version of article.In general, feature points and camera parameters can only be estimated with limited accuracy due to
noisy images. In case of collinear feature points, it is possible to benefit from this geometrical regularity
by correcting the feature points to lie on the supporting estimated straight line, yielding increased accuracy
of the estimated camera parameters. However, regarding Maximum-Likelihood (ML) estimation, this
procedure is incomplete and suboptimal. An optimal solution must also determine the error covariance of
corrected features. In this paper, a complete theoretical covariance propagation analysis starting from the
error of the feature points up to the error of the estimated camera parameters is performed. Additionally,
corresponding Fisher Information Matrices are determined and fundamental relationships between the
number and distance of collinear points and corresponding error variances are revealed algebraically.
To demonstrate the impact of collinearity, experiments are conducted with covariance propagation analyses,
showing significant reduction of the error variances of the estimated parameters.
(C) 2010 Elsevier Inc. All rights reserved
Dictionary-based Tensor Canonical Polyadic Decomposition
To ensure interpretability of extracted sources in tensor decomposition, we
introduce in this paper a dictionary-based tensor canonical polyadic
decomposition which enforces one factor to belong exactly to a known
dictionary. A new formulation of sparse coding is proposed which enables high
dimensional tensors dictionary-based canonical polyadic decomposition. The
benefits of using a dictionary in tensor decomposition models are explored both
in terms of parameter identifiability and estimation accuracy. Performances of
the proposed algorithms are evaluated on the decomposition of simulated data
and the unmixing of hyperspectral images
Order Induced by Dilution in Pyrochlore XY Antiferromagnets
XY pyrochlore antiferromagnets are well-known to exhibit order-by-disorder
through both quantum and thermal selection. In this paper we consider the
effect of substituting non-magnetic ions onto the magnetic sites in a
pyrochlore XY model with generally anisotropic exchange tuned by a single
parameter . The physics is controlled by two points in this
space of parameters at which there are line modes in
the ground state and hence an ground state degeneracy intermediate
between that of a conventional magnet and a Coulomb phase. At each of these
points, single vacancies seed pairs of line defects. Two line defects carrying
incompatible spin configurations from different vacancies can cross leading to
an effective one-dimensional description of the resulting spin texture. In the
thermodynamic limit at finite density, we find that dilution selects a state
"opposite" to the state selected by thermal and quantum disorder which is
understood from the single vacancy limit. The latter finding hints at the
possibility that ErYTiO for small exhibits a second
phase transition within the thermally selected state into a
state selected by the quenched disorder.Comment: 14 pages, 12 figure
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