127 research outputs found
Observations of the post shock break-out emission of SN 2011dh with XMM-Newton
After the occurrence of the type cIIb SN 2011dh in the nearby spiral galaxy M
51 numerous observations were performed with different telescopes in various
bands ranging from radio to gamma-rays. We analysed the XMM-Newton and Swift
observations taken 3 to 30 days after the SN explosion to study the X-ray
spectrum of SN 2011dh. We extracted spectra from the XMM-Newton observations,
which took place ~7 and 11 days after the SN. In addition, we created
integrated Swift/XRT spectra of 3 to 10 days and 11 to 30 days. The spectra are
well fitted with a power-law spectrum absorbed with Galactic foreground
absorption. In addition, we find a harder spectral component in the first
XMM-Newton spectrum taken at t ~ 7 d. This component is also detected in the
first Swift spectrum of t = 3 - 10 d. While the persistent power-law component
can be explained as inverse Compton emission from radio synchrotron emitting
electrons, the harder component is most likely bremsstrahlung emission from the
shocked stellar wind. Therefore, the harder X-ray emission that fades away
after t ~ 10 d can be interpreted as emission from the shocked circumstellar
wind of SN 2011dh.Comment: Accepted for publication as a Research Note in Astronomy and
Astrophysic
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Second-order Democratic Aggregation
Aggregated second-order features extracted from deep convolutional networks
have been shown to be effective for texture generation, fine-grained
recognition, material classification, and scene understanding. In this paper,
we study a class of orderless aggregation functions designed to minimize
interference or equalize contributions in the context of second-order features
and we show that they can be computed just as efficiently as their first-order
counterparts and they have favorable properties over aggregation by summation.
Another line of work has shown that matrix power normalization after
aggregation can significantly improve the generalization of second-order
representations. We show that matrix power normalization implicitly equalizes
contributions during aggregation thus establishing a connection between matrix
normalization techniques and prior work on minimizing interference. Based on
the analysis we present {\gamma}-democratic aggregators that interpolate
between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both
on several classification tasks. Moreover, unlike power normalization, the
{\gamma}-democratic aggregations can be computed in a low dimensional space by
sketching that allows the use of very high-dimensional second-order features.
This results in a state-of-the-art performance on several datasets
Detecting 3D geometric boundaries of indoor scenes under varying lighting
The goal of this research is to identify 3D geometric boundaries in a set of 2D photographs of a static indoor scene under unknown, changing lighting conditions. A 3D geometric boundary is a contour located at a 3D depth discontinuity or a discontinuity in the surface normal. These boundaries can be used effectively for reasoning about the 3D layout of a scene. To distinguish 3D geometric boundaries from 2D texture edges, we analyze the illumination subspace of local appearance at each image location. In indoor time-lapse photography and surveillance video, we frequently see images that are lit by unknown combinations of uncalibrated light sources. We in-troduce an algorithm for semi-binary non-negative matrix factorization (SBNMF) to decompose such images into a set of lighting basis images, each of which shows the scene lit by a single light source. These basis images provide a natural, succinct representation of the scene, enabling tasks such as scene editing (e.g., relighting) and shadow edge identificatio
Riemannian Sparse Coding for Positive Definite Matrices
International audienceInspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches
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