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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMP3KB
Repositorysid.inpe.br/sibgrapi/2015/06.19.21.42
Last Update2015:06.19.21.42.54 (UTC) letriciapsa@gmail.com
Metadatasid.inpe.br/sibgrapi/2015/06.19.21.42.54
Metadata Last Update2020:02.19.02.14.04 (UTC) administrator
Citation KeyChinoAvalRodrTrai:2015:DeFiSt
TitleBoWFire: detection of fire in still images by integrating pixel color and texture analysis
FormatOn-line
Year2015
Access Date2021, Dec. 07
Number of Files1
Size1705 KiB
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Author1 Chino, Daniel Yashinobu Takada
2 Avalhais, Letricia Pereira Soares
3 Rodrigues Junior, Jose Fernando
4 Traina, Agma Juci Machado
Affiliation1 University of Sao Paulo
2 University of Sao Paulo
3 University of Sao Paulo
4 University of Sao Paulo
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressletriciapsa@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 21:42:54 :: letriciapsa@gmail.com -> administrator ::
2020-02-19 02:14:04 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsfire detection
still images
pixel-color classification
texture feature
AbstractEmergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.
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Languageen
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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