Real-time detection of steam in video images

Authors:
R. J. Ferrari;H. Zhang;C. R. Kube
Affiliations:
CIMS Laboratory, Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2P8;CIMS Laboratory, Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2P8;CIMS Laboratory, Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2P8 and Research Department, Syncrude Canada Ltd., Edmonton, AB, Canada T6N 1H4
Venue:
Pattern Recognition
Year:
2007

Citing 13
Cited 10

Characterization of Signals from Multiscale Edges

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A new approach to identify big rocks with applications to the mining industry

Real-Time Imaging
Dynamic Textures

International Journal of Computer Vision
Rotation-invariant texture feature for image retrieval

Computer Vision and Image Understanding
Dynamic Texture Segmentation

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A multistage adaptive thresholding method

Pattern Recognition Letters
Wavelet-based statistical signal processing using hidden Markovmodels

IEEE Transactions on Signal Processing
On the selection of an optimal wavelet basis for texture characterization

IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models

IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

A fast accumulative motion orientation model based on integral image for video smoke detection

Pattern Recognition Letters
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut

Image and Vision Computing
Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees

Pattern Recognition
Minimum classification error learning for sequential data in the wavelet domain

Pattern Recognition
Fast dynamic texture detection

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Transmission: a new feature for computer vision based smoke detection

AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Multiscale information fusion by graph cut through convex optimization

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Global optimization of wavelet-domain hidden Markov tree for image segmentation

Pattern Recognition
A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection

Pattern Recognition
Early smoke detection in video using swaying and diffusion feature

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.01

Visualization

Abstract

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 48x48 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.