Abstract
This paper focuses on the study of fire color spaces and the evaluation of image segmentation methods commonly available in the literature of wildland and urban fires. The evaluation method, based on the determination of a segmentation quality index, is applied on three series of fire images obtained at the usual scales of validation of forest fire models (laboratory scale, fire tunnel scale and field scale). Depending on the considered scale, different methods reveal themselves as being the most appropriate. In this study we present the advantages and drawbacks of different segmentation algorithms and color spaces used in fire detection and characterization.
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References
Séro-Guillaume, O., Ramezani, S., Margerit, J., Calogine, D.: On large scale forest fires propagation models. International Journal of Thermal Sciences 47(6), 680–694 (2008)
Zhou, X., Weise, D., Mahalingamx, S.: Experimental measurements and numerical modeling of marginal burning in live chaparral fuel beds. Proceedings of the Combustion Institute 30, 2287–2294 (2005)
Santoni, P.A., Simeoni, A., Rossi, J.L., Bosseur, F., Morandini, F., Silvani, X., Balbi, J.H., Cancellieri, D., Rossi, L.: Instrumentation of wildland fire: Characterisation of a fire spreading through a Mediterranean shrub. Fire Safety Journal 41(3), 171–184 (2006)
Chetehouna, K., Séro-Guillaume, O., Sochet, I., Degiovanni, A.: On the experimental determination of flame front positions and of propagation parameters for a fire. International Journal of Thermal Sciences 47(9), 1148–1157 (2008)
Silvani, X., Morandini, F.: Fire spread experiments in the field: Temperature and heat fluxes measurements. Fire Safety Journal 44(2), 279–285 (2009)
Martinez-de Dios, J.R., Arrue, B.C., Ollero, A., Merino, L., Gómez-Rodríguez, F.: Computer vision techniques for forest fire perception. Image and Vision Computing 26(4), 550–562 (2008)
Chetehouna, K., Zarguili, I., Séro-Guillaume, O., Giroud, F., Picard, C.: On the two ways for the computing of the fire front positions and the rate of spread. Modelling, Monitoring and Management of Forest Fires. WIT Transactions on Ecology and the Environment 119, 3–12 (2008)
Rossi, L., Akhloufi, M.: Dynamic fire 3D modeling using a real-time stereovision system. In: International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CIS2E 2008), December 5-13 (2008)
Chen, T., Wu, P., Chiou, Y.: An early fire-detection method based on image processing. In: Proceeding of International Conference on Image Processing, ICIP 2004, pp. 1707–1710 (2004)
Ko, B.C., Cheong, K.H., Nam, J.Y.: Fire detection based on vision sensor and support vector machines. Fire Safety Journal 44(3), 322–329 (2009)
Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Safety Journal 44(2), 147–158 (2009)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)
Chabrier, S., Emile, B., Rosenberger, C., Laurent, H.: Unsupervised performance evaluation of image segmentation. EURASIP Journal on Applied Signal Processing, Special issue on performance evaluation in image processing, 1–12 (2006)
Hafiane, A., Chabrier, S., Rosenberger, C., Laurent, H.: A new supervised evaluation criterion for region based segmentation methods. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 439–448. Springer, Heidelberg (2007)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 929–944 (2007)
Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding 110(2), 260–280 (2008)
Chabrier, S., Laurent, H., Emile, B.: Psychovisual evaluation of image segmentation results. In: IEEE Conference on Signal Processing, ICSP (2006)
Vinet, L.: Segmentation et mise en correspondance de regions de paires dimages stereoscopiques, Ph.D. dissertation, Universite de Paris IX Dauphine, Juillet (1991)
Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. In: International Conference on Image Processing (ICIP 1995), Washington, DC, USA, vol. 3, pp. 53–56 (1995)
Yasnoff, W.A., Mui, J.K., Bacus, J.W.: Error measures for scene segmentation. Pattern Recognition 9, 217–231 (1977)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: International Conference on Computer Vision (ICCV), July 2001, pp. 416–423 (2001)
Rudz, S., Chetehouna, K., Séro-Guillaume, O.: Determination of the Flame Fire Front Characteristics by Means of a Flame Model and Inverse Method. In: Proceedings of 6th Mediterranean Combustion Symposium, Corsica, pp. 7–11 (2009)
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Rudz, S., Chetehouna, K., Hafiane, A., Sero-Guillaume, O., Laurent, H. (2009). On the Evaluation of Segmentation Methods for Wildland Fire. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_2
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DOI: https://doi.org/10.1007/978-3-642-04697-1_2
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