4,530 research outputs found
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level
correspondences under difficult imaging conditions. We propose an approach
where a single convolutional neural network plays a dual role: It is
simultaneously a dense feature descriptor and a feature detector. By postponing
the detection to a later stage, the obtained keypoints are more stable than
their traditional counterparts based on early detection of low-level
structures. We show that this model can be trained using pixel correspondences
extracted from readily available large-scale SfM reconstructions, without any
further annotations. The proposed method obtains state-of-the-art performance
on both the difficult Aachen Day-Night localization dataset and the InLoc
indoor localization benchmark, as well as competitive performance on other
benchmarks for image matching and 3D reconstruction.Comment: Accepted at CVPR 201
Permutation-invariant distance between atomic configurations
We present a permutation-invariant distance between atomic configurations,
defined through a functional representation of atomic positions. This distance
enables to directly compare different atomic environments with an arbitrary
number of particles, without going through a space of reduced dimensionality
(i.e. fingerprints) as an intermediate step. Moreover, this distance is
naturally invariant through permutations of atoms, avoiding the time consuming
associated minimization required by other common criteria (like the Root Mean
Square Distance). Finally, the invariance through global rotations is accounted
for by a minimization procedure in the space of rotations solved by Monte Carlo
simulated annealing. A formal framework is also introduced, showing that the
distance we propose verifies the property of a metric on the space of atomic
configurations. Two examples of applications are proposed. The first one
consists in evaluating faithfulness of some fingerprints (or descriptors), i.e.
their capacity to represent the structural information of a configuration. The
second application concerns structural analysis, where our distance proves to
be efficient in discriminating different local structures and even classifying
their degree of similarity
Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services
Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced
services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
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