• Real-time detection of steam i...

Treffer: Real-time detection of steam in video images

Title:
Real-time detection of steam in video images
Authors:
FERRARI, R. J, ZHANG, H, KUBE, C. R
Source:
Pattern recognition. 40(3):1148-1159
Publisher Information:
Oxford: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 30 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Traitement du signal, Signal processing, Reconnaissance des formes, Pattern recognition, Traitement des images, Image processing, Divers, Miscellaneous, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Classification image, Image classification, Classification signal, Signal classification, Image floue, Blurred image, Imagen borrosa, Machine vecteur support, Support vector machine, Máquina vector soporte, Modèle Markov variable cachée, Hidden Markov models, Modèle Markov, Markov model, Modelo Markov, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Sable, Sand, Arena, Segmentation image, Image segmentation, Signal vidéo, Video signal, Señal video, Séquence image, Image sequence, Secuencia imagen, Texture, Textura, Traitement automatique, Automatic processing, Tratamiento automático, Traitement image, Image processing, Procesamiento imagen, Traitement informatique, Computerized processing, Tratamiento informático, Traitement signal vidéo, Video signal processing, Traitement signal, Signal processing, Procesamiento señal, Traitement temps réel, Real time processing, Tratamiento tiempo real, Transformation ondelette, Wavelet transformation, Transformación ondita, Detection of dynamic texture, Oil sand, Steam detection, Video segmentation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
CIMS Laboratory, Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2P8, Canada
Research Department, Syncrude Canada Ltd, Edmonton, AB, T6N 1H4, Canada
ISSN:
0031-3203
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18425928
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.18425928
Database:
PASCAL Archive

Weitere Informationen

In this paper, we present a real-time image processing technique for the detection of steam in video images. The assumption made is that the presence of steam acts as a blurring process, which changes the local texture pattern of an image while reducing the amount of details. The problem of detecting steam is treated as a supervised pattern recognition problem. A statistical hidden Markov tree (HMT) model derived from the coefficients of the dual-tree complex wavelet transform (DT-CWT) in small 48 x 48 local regions of the image frames is used to characterize the steam texture pattern. The parameters of the HMT model are used as an input feature vector to a support vector machine (SVM) technique, specially tailored for this purpose. By detecting and determining the total area covered by steam in a video frame, a computerized image processing system can automatically decide if the frame can be used for further analysis. The proposed method was quantitatively evaluated by using a labelled image data set with video frames sampled from a real oil sand video stream. The classification results were 90% correct when compared to human labelled image frames. The technique is useful as a pre-processing step in automated image processing systems.