An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing

The determination of flame or fire edges is the process of identifying a boundary between the area where there is thermochemical reaction and those without. Previous vision based methods were based on color difference, motion detection of flame pixel and flame edge detection Most previous visionbased methods using color information and temporal variations of pixels produce frequent false alarms due to the use of many heuristic features. Plus, there is usually a computation delay for accurate fire detection. Thus, to overcome these problems, candidate fire regions are first detected using a background model and color model of fire. Probabilistic models of fire are then generated based on the fact that fire pixel values in consecutive frames change constantly and these models are applied to a Bayesian Network. This proposed work uses a threelevel Naïve Bayesian Network that contains intermediate nodes, and uses four probability density functions for evidence at each node. The probability density functions for each node are modeled using the skewness of the color red and three high frequency components obtained from a wavelet transform. The proposed system was sucessfully applied to various tasks in real-world environments and effectively distinguished fire from fire-colored objects. Experimental results will indicate that the proposed method outperforms other methods in both of fire target enhancement and background detail preservation.