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Peer Evaluation : Wavelet-based smoke detection in outdoor video sequences
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    Wavelet-based smoke detection in outdoor video sequences

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    In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application.

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    Description

    Notice (8): Undefined index: Affiliation [APP/views/pdflibraries/read.ctp, line 562]
    Title : Wavelet-based smoke detection in outdoor video sequences
    Author(s) : R Gonzalez-Gonzalez, V Alarcon-Aquino, R Rosas-Romero, O Starostenko, J Rodriguez-Asomoza, J M Ramirez-Cortes
    Abstract : In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application.
    Keywords : bicubic interpolation algorithm, gray scale transformation, image indexation, image preprocessing, image sequences, interpolation, regions interest, smoke detection algorithm, smoke detectors, smoke verification algorithm, stationary wavelet transform, vi

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2010

    Notice (8): Undefined variable: affiliations_output [APP/views/pdflibraries/read.ctp, line 595]
    Conference_title : Circuits and Systems MWSCAS 2010 53rd IEEE International Midwest Symposium on
    Pages : 383-387
    Url : http://api.mendeley.com/research/waveletbased-smoke-detection-outdoor-video-sequences/

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