43,001 research outputs found
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
Fast Selection of Spectral Variables with B-Spline Compression
The large number of spectral variables in most data sets encountered in
spectral chemometrics often renders the prediction of a dependent variable
uneasy. The number of variables hopefully can be reduced, by using either
projection techniques or selection methods; the latter allow for the
interpretation of the selected variables. Since the optimal approach of testing
all possible subsets of variables with the prediction model is intractable, an
incremental selection approach using a nonparametric statistics is a good
option, as it avoids the computationally intensive use of the model itself. It
has two drawbacks however: the number of groups of variables to test is still
huge, and colinearities can make the results unstable. To overcome these
limitations, this paper presents a method to select groups of spectral
variables. It consists in a forward-backward procedure applied to the
coefficients of a B-Spline representation of the spectra. The criterion used in
the forward-backward procedure is the mutual information, allowing to find
nonlinear dependencies between variables, on the contrary of the generally used
correlation. The spline representation is used to get interpretability of the
results, as groups of consecutive spectral variables will be selected. The
experiments conducted on NIR spectra from fescue grass and diesel fuels show
that the method provides clearly identified groups of selected variables,
making interpretation easy, while keeping a low computational load. The
prediction performances obtained using the selected coefficients are higher
than those obtained by the same method applied directly to the original
variables and similar to those obtained using traditional models, although
using significantly less spectral variables
The Extremely Luminous Quasar Survey in the Pan-STARRS 1 Footprint (PS-ELQS)
We present the results of the Extremely Luminous Quasar Survey in the
survey of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS;
PS1). This effort applies the successful quasar selection strategy of the
Extremely Luminous Survey in the Sloan Digital Sky Survey footprint
() to a much larger area
(). This spectroscopic survey targets the most
luminous quasars (; ) at intermediate redshifts
(). Candidates are selected based on a near-infrared JKW2 color cut
using WISE AllWISE and 2MASS photometry to mainly reject stellar contaminants.
Photometric redshifts () and star-quasar classifications for each
candidate are calculated from near-infrared and optical photometry using the
supervised machine learning technique random forests. We select 806 quasar
candidates at from a parent sample of 74318 sources. After
exclusion of known sources and rejection of candidates with unreliable
photometry, we have taken optical identification spectra for 290 of our 334
good PS-ELQS candidates. We report the discovery of 190 new quasars
and an additional 28 quasars at lower redshifts. A total of 44 good PS-ELQS
candidates remain unobserved. Including all known quasars at , our
quasar selection method has a selection efficiency of at least . At lower
declinations we approximately triple the known
population of extremely luminous quasars. We provide the PS-ELQS quasar catalog
with a total of 592 luminous quasars (, ). This unique
sample will not only be able to provide constraints on the volume density and
quasar clustering of extremely luminous quasars, but also offers valuable
targets for studies of the intergalactic medium.Comment: 34 pages, 10 figures, accepted to ApJ
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