Fire Detection Algorithms Using Multimodal ... - Bilkent University

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BIBLIOGRAPHY 117[67] L. Rabiner and B.-H. Juang. Fundamentals of Speech Recognition. Prentice-Hall Inc., 1993.[68] A. Rahman and M. Murshed. Real-time temporal texture characterisationusing block-based motion co-occurrence statistics. In Proceedings of theIEEE International Conference on Image Processing (ICIP), pages 1593–1596, 2004.[69] D. Reynolds and R. Rose. Robust text-independent speaker identificationusing gaussian mixture speaker models. IEEE Transactions on Speech andAudio Processing, 3(1):72–83, 1995.[70] Y. Sahin. Animals as mobile biological sensors for forest fire detection. Sensors,7(12):3084–3099, 2007.[71] P. Saisan, G. Doretto, Y. Wu, and S. Soatto. Dynamic texture recognition.In Proceedings of the IEEE Conference on Computer Vision and PatternRecognition (CVPR), volume 2, pages 58–63, December 2001.[72] J. Schlimmer and R. Granger. Incremental learning from noisy data. MachineLearning, 1, page 317354, 1986.[73] B. Schnaufer and W. Jenkins. New data-reusing LMS algorithms for improvedconvergence. In Proceedings of the Asilomar Conference, PacificGroves, CA, pages 1584–1588, 1993.[74] A. Singer and M. Feder. Universal linear prediction by model order weighting.IEEE Transactions on Signal Processing, 47-10:2685–2699, 1999.[75] J. Smith, C.-Y. Lin, and M. Naphade. Video texture indexing using spatiotemporalwavelets. In Proceedings of the IEEE International Conferenceon Image Processing (ICIP), volume 2, pages 437–440, 2002.[76] L. St-Laurent, D. Prvost, and X. Maldague. Thermal imaging for enhancedforeground-background segmentation. In Proceedings of the 8-th InternationalConference on Quantitative InfraRed Thermography, 2006.
BIBLIOGRAPHY 118[77] C. Stauffer and W. Grimson. Adaptive background mixture models for realtimetracking. In Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition (CVPR), volume 2, 1999.[78] W. Straumann, D. Rizzotti, and N. Schibli. Method and Device for DetectingFires Based on Image Analysis. European Patent EP 1,364,351, 2002.[79] M. Szummer and R. W. Picard. Temporal texture modeling. In Proceedingsof the IEEE International Conference on Image Processing (ICIP), volume 3,pages 823–826, 1996.[80] Y. Takatoshi. Fire Detecting Device. Japanese Patent 11,144,167, 1999.[81] M. Thuillard. Method and Device for Detecting Fires Based on Image Analysis.US Patent 6,011,464, 1998.[82] M. Thuillard. A new flame detector using the latest research on flames andfuzzy-wavelet algorithms. Fire Safety Journal, 37:371–380, 2002.[83] B. Töreyin. Moving object detection and tracking in wavelet compressedvideo. Master’s thesis, Bilkent University, Department of Electrical andElectronics Engineering, Ankara, Turkey, 2003.[84] B. Töreyin, Y. Dedeoglu, and A. Cetin. Flame detection in video usinghidden markov models. In Proceedings of the IEEE International Conferenceon Image Processing (ICIP), pages 1230–1233, 2005.[85] B. Töreyin, Y. Dedeoglu, and A. Cetin. HMM Based Falling Person DetectionUsing Both Audio and Video. In Proceedings of the IEEE InternationalWorkshop on Human-Computer Interaction (HCI), volume 3766, pages 211–220, 2005.[86] B. Töreyin, Y. Dedeoglu, and A. Cetin. Wavelet based real-time smokedetection in video. In Proceedings of the 13-th European Signal ProcessingConference (EUSIPCO), 2005.[87] B. Töreyin, Y. Dedeoglu, U. Gudukbay, and A. Cetin. Computer visionbased system for real-time fire and flame detection. Pattern RecognitionLetters, 27:49–58, 2006.
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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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