1,183,056 research outputs found
Dempster-Shafer based multi-view occupancy maps
Presented is a method for calculating occupancy maps with a set of calibrated and synchronised cameras. In particular, Dempster-Shafer based fusion of the ground occupancies computed from each view is proposed. The method yields very accurate occupancy detection results and in terms of concentration of the occupancy evidence around ground truth person positions it outperforms the state-of-the- art probabilistic occupancy map method and fusion by summing
Navigating through archives, libraries and museums: Topic Maps as a harmonizing instrument
The paper deals with the possibility of creating a topic map based system where different sectors of cultural heritage would interact with users, by monitoring the navigation histories of users and the statistics on the searches, in order to authorize variant form of names. The problem of managing different sectors and harmonizing them both from a structural and a semantic view point, by using Topic Maps, is also discussed. With regards to this, we are introducing two projects, which are largely based on the above mention use of Topic Maps.
The original publication is available at www.springerlink.com http://www.springerlink.com/content/6k5473124678k452/fulltext.pd
NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps
Molecular biology knowledge can be systematically represented in a
computer-readable form as a comprehensive map of molecular interactions. There
exist a number of maps of molecular interactions containing detailed
description of various cell mechanisms. It is difficult to explore these large
maps, to comment their content and to maintain them. Though there exist several
tools addressing these problems individually, the scientific community still
lacks an environment that combines these three capabilities together. NaviCell
is a web-based environment for exploiting large maps of molecular interactions,
created in CellDesigner, allowing their easy exploration, curation and
maintenance. NaviCell combines three features: (1) efficient map browsing based
on Google Maps engine; (2) semantic zooming for viewing different levels of
details or of abstraction of the map and (3) integrated web-based blog for
collecting the community feedback. NaviCell can be easily used by experts in
the field of molecular biology for studying molecular entities of their
interest in the context of signaling pathways and cross-talks between pathways
within a global signaling network. NaviCell allows both exploration of detailed
molecular mechanisms represented on the map and a more abstract view of the map
up to a top-level modular representation. NaviCell facilitates curation,
maintenance and updating the comprehensive maps of molecular interactions in an
interactive fashion due to an imbedded blogging system. NaviCell provides an
easy way to explore large-scale maps of molecular interactions, thanks to the
Google Maps and WordPress interfaces, already familiar to many users. Semantic
zooming used for navigating geographical maps is adopted for molecular maps in
NaviCell, making any level of visualization meaningful to the user. In
addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte
A tour about Isaac Newton's life
Here we propose a tour about the life of Isaac Newton, using a georeferenced
method, based on the free satellite maps. Our tour is modelled on the time-line
of the great scientist's life, as an ancient "itinerarium" was modelled on the
Roman roads, providing a listing of places and intervening distances, sometimes
with short description or symbols concerning the places. KML language and
Google Earth, with its Street View and 3D images are powerful tools to create
this virtual tour.Comment: Georeferencing, Satellite Maps, KML, XML, Acme Mapper, History of
Physic
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
A detailed environment perception is a crucial component of automated
vehicles. However, to deal with the amount of perceived information, we also
require segmentation strategies. Based on a grid map environment
representation, well-suited for sensor fusion, free-space estimation and
machine learning, we detect and classify objects using deep convolutional
neural networks. As input for our networks we use a multi-layer grid map
efficiently encoding 3D range sensor information. The inference output consists
of a list of rotated bounding boxes with associated semantic classes. We
conduct extensive ablation studies, highlight important design considerations
when using grid maps and evaluate our models on the KITTI Bird's Eye View
benchmark. Qualitative and quantitative benchmark results show that we achieve
robust detection and state of the art accuracy solely using top-view grid maps
from range sensor data.Comment: 6 pages, 4 tables, 4 figure
Image-based Geolocalization by Ground-to-2.5D Map Matching
We study the image-based geolocalization problem, aiming to localize
ground-view query images on cartographic maps. Current methods often utilize
cross-view localization techniques to match ground-view query images with 2D
maps. However, the performance of these methods is unsatisfactory due to
significant cross-view appearance differences. In this paper, we lift
cross-view matching to a 2.5D space, where heights of structures (e.g., trees
and buildings) provide geometric information to guide the cross-view matching.
We propose a new approach to learning representative embeddings from
multi-modal data. Specifically, we establish a projection relationship between
2.5D space and 2D aerial-view space. The projection is further used to combine
multi-modal features from the 2.5D and 2D maps using an effective
pixel-to-point fusion method. By encoding crucial geometric cues, our method
learns discriminative location embeddings for matching panoramic images and
maps. Additionally, we construct the first large-scale ground-to-2.5D map
geolocalization dataset to validate our method and facilitate future research.
Both single-image based and route based localization experiments are conducted
to test our method. Extensive experiments demonstrate that the proposed method
achieves significantly higher localization accuracy and faster convergence than
previous 2D map-based approaches
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