70,081 research outputs found
Shape: A 3D Modeling Tool for Astrophysics
We present a flexible interactive 3D morpho-kinematical modeling application
for astrophysics. Compared to other systems, our application reduces the
restrictions on the physical assumptions, data type and amount that is required
for a reconstruction of an object's morphology. It is one of the first publicly
available tools to apply interactive graphics to astrophysical modeling. The
tool allows astrophysicists to provide a-priori knowledge about the object by
interactively defining 3D structural elements. By direct comparison of model
prediction with observational data, model parameters can then be automatically
optimized to fit the observation. The tool has already been successfully used
in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE
Transactions on Visualization and Computer Graphics
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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