63,577 research outputs found
T-PHOT version 2.0: improved algorithms for background subtraction, local convolution, kernel registration, and new options
We present the new release v2.0 of T-PHOT, a publicly available software
package developed to perform PSF-matched, prior-based, multiwavelength
deconfusion photometry of extragalactic fields. New features included in the
code are presented and discussed: background estimation, fitting using position
dependent kernels, flux prioring, diagnostical statistics on the residual
image, exclusion of selected sources from the model and residual images,
individual registration of fitted objects. These new options improve on the
performance of the code, allowing for more accurate results and providing
useful aids for diagnostics.Comment: 7 pages, 8 figure
An Imprint of Molecular Cloud Magnetization in the Morphology of the Dust Polarized Emission
We describe a morphological imprint of magnetization found when considering
the relative orientation of the magnetic field direction with respect to the
density structures in simulated turbulent molecular clouds. This imprint was
found using the Histogram of Relative Orientations (HRO): a new technique that
utilizes the gradient to characterize the directionality of density and column
density structures on multiple scales. We present results of the HRO analysis
in three models of molecular clouds in which the initial magnetic field
strength is varied, but an identical initial turbulent velocity field is
introduced, which subsequently decays. The HRO analysis was applied to the
simulated data cubes and mock-observations of the simulations produced by
integrating the data cube along particular lines of sight. In the 3D analysis
we describe the relative orientation of the magnetic field with
respect to the density structures, showing that: 1.The magnetic field shows a
preferential orientation parallel to most of the density structures in the
three simulated cubes. 2.The relative orientation changes from parallel to
perpendicular in regions with density over a critical density in the
highest magnetization case. 3.The change of relative orientation is largest for
the highest magnetization and decreases in lower magnetization cases. This
change in the relative orientation is also present in the projected maps. In
conjunction with simulations HROs can be used to establish a link between the
observed morphology in polarization maps and the physics included in
simulations of molecular clouds.Comment: (16 pages, 11 figures, submitted to ApJ 05MAR2013, accepted
07JUL2013
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
Localizing Region-Based Active Contours
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.2004611In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Cataloging the neuronal cell types that comprise circuitry of individual
brain regions is a major goal of modern neuroscience and the BRAIN initiative.
Single-cell RNA sequencing can now be used to measure the gene expression
profiles of individual neurons and to categorize neurons based on their gene
expression profiles. While the single-cell techniques are extremely powerful
and hold great promise, they are currently still labor intensive, have a high
cost per cell, and, most importantly, do not provide information on spatial
distribution of cell types in specific regions of the brain. We propose a
complementary approach that uses computational methods to infer the cell types
and their gene expression profiles through analysis of brain-wide single-cell
resolution in situ hybridization (ISH) imagery contained in the Allen Brain
Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH
image for each gene and model it as a spatial point process mixture, whose
mixture weights are given by the cell types which express that gene. By fitting
a point process mixture model jointly to the ISH images, we infer both the
spatial point process distribution for each cell type and their gene expression
profile. We validate our predictions of cell type-specific gene expression
profiles using single cell RNA sequencing data, recently published for the
mouse somatosensory cortex. Jointly with the gene expression profiles, cell
features such as cell size, orientation, intensity and local density level are
inferred per cell type
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