Forest Fire Smog Feature Extraction Based on Pulse-Coupled Neural Network
A novel algorithm for image-based forest fire smog feature extraction based on Pulse-Coupled neural network (PCNN) is proposed. The PCNN is derived from the phenomena of synchronous pulse burst in mammals visual cortex. The outputs of PCNN represent unique features of imported images, and has been proved to be invariant to translation, rotation and distortion. In this paper the image from video surveillance monitor is split, into three dimensions in RGB color space, then input each dimension to PCNN to extract texture feature, the one-class support vector machine is applied to predict the feature in order to test features accuracy. The experimental results show that the algorithm we propose accurately distinguishes smog and non-smog images which outperform both the traditional Euclidean distance algorithms and the algorithms based on grey level co-occurrence matrix (GLCM) we applied in our earlier study. The recognition accuracy is 98％ with robustness on our smog image database.
Forest fire smog recognition Pulse-Coupled neural network (PCNN) Support vector machine (SVM) Feature extraction Pattern recognition
Wu Jiang Huang Rule Xu Ziyue Han Ning
School of Science Beijing Forestry University Beijing, China Information Centre Beijing Forestry University Beijing, China School of Technology Beijing Forestry University Beijing, China
国际会议
重庆
英文
186-189
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）