8,173 research outputs found
Image Parsing with a Wide Range of Classes and Scene-Level Context
This paper presents a nonparametric scene parsing approach that improves the
overall accuracy, as well as the coverage of foreground classes in scene
images. We first improve the label likelihood estimates at superpixels by
merging likelihood scores from different probabilistic classifiers. This boosts
the classification performance and enriches the representation of
less-represented classes. Our second contribution consists of incorporating
semantic context in the parsing process through global label costs. Our method
does not rely on image retrieval sets but rather assigns a global likelihood
estimate to each label, which is plugged into the overall energy function. We
evaluate our system on two large-scale datasets, SIFTflow and LMSun. We achieve
state-of-the-art performance on the SIFTflow dataset and near-record results on
LMSun.Comment: Published at CVPR 2015, Computer Vision and Pattern Recognition
(CVPR), 2015 IEEE Conference o
Find your Way by Observing the Sun and Other Semantic Cues
In this paper we present a robust, efficient and affordable approach to
self-localization which does not require neither GPS nor knowledge about the
appearance of the world. Towards this goal, we utilize freely available
cartographic maps and derive a probabilistic model that exploits semantic cues
in the form of sun direction, presence of an intersection, road type, speed
limit as well as the ego-car trajectory in order to produce very reliable
localization results. Our experimental evaluation shows that our approach can
localize much faster (in terms of driving time) with less computation and more
robustly than competing approaches, which ignore semantic information
OLT: A Toolkit for Object Labeling Applied to Robotic RGB-D Datasets
In this work we present the Object Labeling Toolkit
(OLT), a set of software components publicly available for
helping in the management and labeling of sequential RGB-D
observations collected by a mobile robot. Such a robot can be
equipped with an arbitrary number of RGB-D devices, possibly
integrating other sensors (e.g. odometry, 2D laser scanners,
etc.). OLT first merges the robot observations to generate a
3D reconstruction of the scene from which object segmentation
and labeling is conveniently accomplished. The annotated labels
are automatically propagated by the toolkit to each RGB-D
observation in the collected sequence, providing a dense labeling
of both intensity and depth images. The resulting objects’ labels
can be exploited for many robotic oriented applications, including
high-level decision making, semantic mapping, or contextual
object recognition. Software components within OLT are highly
customizable and expandable, facilitating the integration of
already-developed algorithms. To illustrate the toolkit suitability,
we describe its application to robotic RGB-D sequences taken in
a home environment.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech. Spanish grant pro-
gram FPU-MICINN 2010 and the Spanish projects TAROTH:
New developments toward a Robot at Home (DPI2011-25483)
and PROMOVE: Advances in mobile robotics for promoting
independent life of elders (DPI2014-55826-R
Automated construction and analysis of political networks via open government and media sources
We present a tool to generate real world political networks from user provided lists of politicians and news sites. Additional output includes visualizations, interactive tools and maps that allow a user to better understand the politicians and their surrounding environments as portrayed by the media. As a case study, we construct a comprehensive list of current Texas politicians, select news sites that convey a spectrum of political viewpoints covering Texas politics, and examine the results. We propose a ”Combined” co-occurrence distance metric to better reflect the relationship between two entities. A topic modeling technique is also proposed as a novel, automated way of labeling communities that exist within a politician’s ”extended” network.Peer ReviewedPostprint (author's final draft
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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