Real-time detection of steam in video images

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 48×4848×48 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 9090% correct when compared to human labelled image frames. The technique is useful as a pre-processing step in automated image processing systems.