8,210 research outputs found
Techniques for effective and efficient fire detection from social media images
Social media could provide valuable information to support decision making in
crisis management, such as in accidents, explosions and fires. However, much of
the data from social media are images, which are uploaded in a rate that makes
it impossible for human beings to analyze them. Despite the many works on image
analysis, there are no fire detection studies on social media. To fill this
gap, we propose the use and evaluation of a broad set of content-based image
retrieval and classification techniques for fire detection. Our main
contributions are: (i) the development of the Fast-Fire Detection method
(FFDnR), which combines feature extractor and evaluation functions to support
instance-based learning, (ii) the construction of an annotated set of images
with ground-truth depicting fire occurrences -- the FlickrFire dataset, and
(iii) the evaluation of 36 efficient image descriptors for fire detection.
Using real data from Flickr, our results showed that FFDnR was able to achieve
a precision for fire detection comparable to that of human annotators.
Therefore, our work shall provide a solid basis for further developments on
monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise
Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian
Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano
Traina, 2015, Techniques for effective and efficient fire detection from
social media images, ICEIS, 34-4
Materials and Methods of the Study of Influence of Agrotechnical Methods on Sensory Characteristics of Technical Sorts of Grape
The topicality of using the sensory analysis of berries in enological practice at planning of agrotechnical complex at vineyard to receive the certain style and quality of production was grounded. For study of the influence of agrotechnical methods on sensory characteristics of technical sorts of grape Zagrey and Fragrant, selected by NSC “IVaW named after V.E. Tairov” (Ukraine), there was elaborated the algorithm of research, including field experiment and laboratory sensory analysis. The method of organoleptic analysis of berries, consisted of 20 parameters for assessment of visual, tactile and gustatory properties of pulp, peel and seeds, was approbated. Mathematical processing of experimental data was carried out by the methods of one- and two-factor analysis of variance and analysis of main components in the environment of package of applied programs MS Excell 2010, Statistica Statsoft ver. 7. 0 (Tulsa, USA).The sensory descriptors, characterizing the quality of studied sorts of grape, were determined. It was established, that agrotechnical methods of planting grape bushes influenced the sensory characteristics of berries of studied sorts
Unsupervised landmark analysis for jump detection in molecular dynamics simulations
Molecular dynamics is a versatile and powerful method to study diffusion in
solid-state ionic conductors, requiring minimal prior knowledge of equilibrium
or transition states of the system's free energy surface. However, the analysis
of trajectories for relevant but rare events, such as a jump of the diffusing
mobile ion, is still rather cumbersome, requiring prior knowledge of the
diffusive process in order to get meaningful results. In this work, we present
a novel approach to detect the relevant events in a diffusive system without
assuming prior information regarding the underlying process. We start from a
projection of the atomic coordinates into a landmark basis to identify the
dominant features in a mobile ion's environment. Subsequent clustering in
landmark space enables a discretization of any trajectory into a sequence of
distinct states. As a final step, the use of the smooth overlap of atomic
positions descriptor allows distinguishing between different environments in a
straightforward way. We apply this algorithm to ten Li-ionic systems and
conduct in-depth analyses of cubic LiLaZrO, tetragonal
LiGePS, and the -eucryptite LiAlSiO. We
compare our results to existing methods, underscoring strong points,
weaknesses, and insights into the diffusive behavior of the ionic conduction in
the materials investigated
Damages of Digitized Historical Images as Objects for Content Based Applications
This work presents the preliminary results achieved within a FIRB project aimed to develop innovative support tools for automatic or semi-automatic restoration of damaged digital images oncerning archaeological and monumental inheritance of Mediterranean coast. In particular, this paper is focused on a methodology for describing image degradation and its meta-representation for content based storing and retrieval.
Our innovative idea is to decompose and store in a conventional RDBMS the images content, considering the damages as objects of the images. Moreover, a set of descriptors(a subset of MPEG7 descriptors) is used for the damage meta representation aimed to content based application. Finally we developed a user-friendly database management tool for manipulating the contents of the database
Kansas environmental and resource study: A Great Plains model, tasks 1-6
There are no author identified significant results in this report. Environmental and resources investigations in Kansas utilizing ERTS-1 imagery are summarized for the following areas: (1) use of feature extraction techniqued for texture context information in ERTS imagery; (2) interpretation and automatic image enhancement; (3) water use, production, and disease detection and predictions for wheat; (4) ERTS-1 agricultural statistics; (5) monitoring fresh water resources; and (6) ground pattern analysis in the Great Plains
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