Fire Detection Algorithms Using Multimodal ... - Bilkent University

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BIBLIOGRAPHY 113[28] R. Fablet and P. Bouthemy. Motion recognition using nonparametric imagemotion models estimated from temporal and multiscale co-occurrencestatistics. IEEE Transactions on Pattern Analysis and Machine Intelligence,25:1619–1624, 2003.[29] S. Fazekas and D. Chetverikov. Analysis and performance evaluation of opticalflow features for dynamic texture recognition. Signal Processing:ImageCommunication, 22(7-8):680–691, 2007.[30] R. Fisher (Ed.). CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision,http://homepages.inf.ed.ac.uk/rbf/cvonline/, Accessed at December2008.[31] G. L. Foresti, C. S. Regazzoni, and P. K. V. (Eds.). Multisensor SurveillanceSystems - The Fusion Perspective. Kluwer Academic Publishers, 2003.[32] D. Forsyth and J.Ponce. Computer Vision: A Modern Approach. PrenticeHall, 2002.[33] K. Fujita and S. Nayar. Recognition of dynamic textures using impulseresponses of state variables. In Proceedings of the Third International Workshopon Texture Analysis and Synthesis (Texture), pages 31–36, 2003.[34] O. N. Gerek and A. E. Cetin. Adaptive polyphase subband decompositionstructures for image compression. IEEE Transactions on Image Processing,9:1649–1659, October 2000.[35] A. Ghoreyshi and R. Vidal. Segmenting Dynamic Textures with Ising Descriptors,ARX Models and Level Sets. In Proceedings of the European Conferenceon Computer Vision / Dynamical Vision Workshop (ECCV/DV),2006.[36] P. Guillemant and J. Vicente. Real-time identification of smoke images byclustering motions on a fractal curve with a temporal embedding method.Optical Engineering, 40:554–563, 2001.[37] S. Haykin. Adaptive Filter Theory. Prentice Hall, 2002.
BIBLIOGRAPHY 114[38] G. Healey, D. Slater, T. Lin, B. Drda, and A. Goedeke. A system for realtimefire detection. In Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition (CVPR), pages 15–17, 1993.[39] M. Hefeeda and M. Bagheri. Wireless sensor networks for early detectionof forest fires. In Proceedings of the International Workshop on Mobile Adhoc and Sensor Systems for Global and Homeland Security (MASS-GHS),October 2007.[40] F. Heijden. Image Based Measurement Systems: Object Recognition andParameter Estimation. Wiley, 1996.[41] T. Horprasert, D. Harwood, and L. Davis. A statistical approach for realtimerobust background subtraction and shadow detection. In Proceedingsof the IEEE International Conference on Computer Vision/FRAME-RATEWorkshop (CVPR/FR), 1999.[42] H. Huang, Y. Yan, G. Lu, and A. Reed. On-line flicker measurement ofgaseous flames by imaging processing and spectral analysis. MeasurementScience and Technology, 10:726–733, 1999.[43] O. Javed and M. Shah. Tracking and object classification for automatedsurveillance. In Proceedings of the European Conference on Computer Vision(ECCV), pages 343–357, 2002.[44] S. Khare and S. Negi. Thermal (infrared) imaging sensors. Defence ScienceJournal, 57(3):173–183, 2007.[45] C. Kim, R. Ansari, and A. Cetin. A class of linear-phase regular biorthogonalwavelets. In Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP), pages 673–676, 1992.[46] J. Li, Q. Qi, X. Zou, H. Peng, L. Jiang, and Y. Liang. Technique for automaticforest fire surveillance using visible light image. In Proceedings of theInternational Geoscience and Remote Sensing Symposium, volume 5, pages31–35, 2005.
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viiiçin de işaret işleme yöntem
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ixThis work was supported in part b
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LIST OF FIGURESxiv2.6 Three-state M
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LIST OF FIGURESxvi4.4 Two moving ob
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List of Tables1.1 Organization of t
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Chapter 1IntroductionDynamic textur
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CHAPTER 1. INTRODUCTION 3No fire de
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Chapter 2Flame Detection in Visible
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CHAPTER 2. FLAME DETECTION IN VISIB
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CHAPTER 3. FLAME DETECTION IN INFRA
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Chapter 4Short Range Smoke Detectio
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CHAPTER 4. SHORT RANGE SMOKE DETECT
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