5,480 research outputs found
Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%
Support Vector Machine classification of strong gravitational lenses
The imminent advent of very large-scale optical sky surveys, such as Euclid
and LSST, makes it important to find efficient ways of discovering rare objects
such as strong gravitational lens systems, where a background object is
multiply gravitationally imaged by a foreground mass. As well as finding the
lens systems, it is important to reject false positives due to intrinsic
structure in galaxies, and much work is in progress with machine learning
algorithms such as neural networks in order to achieve both these aims. We
present and discuss a Support Vector Machine (SVM) algorithm which makes use of
a Gabor filterbank in order to provide learning criteria for separation of
lenses and non-lenses, and demonstrate using blind challenges that under
certain circumstances it is a particularly efficient algorithm for rejecting
false positives. We compare the SVM engine with a large-scale human examination
of 100000 simulated lenses in a challenge dataset, and also apply the SVM
method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA
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