A computer vision system for the characterization and classification of flames in glass furnaces

It is pointed out the characterization and classification of certain phenomena that occur inside glass furnaces is of the utmost importance for the design of efficient and energy-saving control strategies. The authors describe a computer vision system dealing with both flame analysis and classification problems. The system was first tested in a laboratory environment where different operating conditions were created through the variation of the burner geometry, the number of active burners, and the rate of fuel used. Two classifiers, one based on a Bayesian formalism and the other on neural networks, were designed and tuned in a laboratory environment. In a second phase, images from an industrial furnace were acquired, several flame classes were defined, and the classifiers were tested. The results obtained by both the Bayesian and the neural network classifiers are quite encouraging and the success rates are similar for both of them. >