Fire Detection Algorithms Using Multimodal ... - Bilkent University
Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University
BIBLIOGRAPHY 119[88] B. Töreyin, E. Soyer, I. Onaran, and A. Cetin. Falling person detectionusing multi-sensor signal processing. EURASIP Journal on Advances inSignal Processing, 2008(1):1–10, 2008.[89] R. Vidal and A. Ravichandran. Optical flow estimation and segmentation ofmultiple moving dynamic textures. In Proceedings of the IEEE Conferenceon Computer Vision and Pattern Recognition (CVPR), volume 2, pages 516–521, 2005.[90] L.-Y. Wei and M. Levoy. Fast texture synthesis using tree-structured vectorquantization. In Proceedings of the 27-th International Conference onComputer Graphics and Interactive Techniques (ACM SIGGRAPH), pages479–488, 2000.[91] B. Widrow and M. Hoff. Adaptive switching circuits. In Proceedings of theIRE WESCON (New York Convention Record), volume 4, pages 96–104,1960.[92] B. Widrow, J. McCool, M. Larimore, and C. Johnson. Stationary and nonstationarylearning characteristics of the LMS adaptive filter. Proceedings ofthe IEEE, 64(8):1151–1162, 1976.[93] B. Widrow and S. Stearns. Adaptive Signal Processing. Prentice Hall, 1985.[94] Z. Xiong, R. Caballero, H. Wang, A. Finn, M. Lelic, and P.-Y. Peng. Video based Smoke Detection: Possibilities, Techniques,and Challenges, available at http://vision.ai.uiuc.edu/∼wanghc/papers/smoke_detection.pdf, Accessed at December 2008.[95] D. Youla and H. Webb. Image restoration by the method of convex projections,Part I-Theory. IEEE Transactions on Medical Imaging, MI-I-2:81–94,1982.[96] J. Zhong and S. Sclaroff. Segmenting foreground objects from a dynamictextured background via a robust kalman filter. In Proceedings of the InternationalConference on Computer Vision (ICCV), pages 44–50, 2003.
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BIBLIOGRAPHY 119[88] B. Töreyin, E. Soyer, I. Onaran, and A. Cetin. Falling person detectionusing multi-sensor signal processing. EURASIP Journal on Advances inSignal Processing, 2008(1):1–10, 2008.[89] R. Vidal and A. Ravichandran. Optical flow estimation and segmentation ofmultiple moving dynamic textures. In Proceedings of the IEEE Conferenceon Computer Vision and Pattern Recognition (CVPR), volume 2, pages 516–521, 2005.[90] L.-Y. Wei and M. Levoy. Fast texture synthesis using tree-structured vectorquantization. In Proceedings of the 27-th International Conference onComputer Graphics and Interactive Techniques (ACM SIGGRAPH), pages479–488, 2000.[91] B. Widrow and M. Hoff. Adaptive switching circuits. In Proceedings of theIRE WESCON (New York Convention Record), volume 4, pages 96–104,1960.[92] B. Widrow, J. McCool, M. Larimore, and C. Johnson. Stationary and nonstationarylearning characteristics of the LMS adaptive filter. Proceedings ofthe IEEE, 64(8):1151–1162, 1976.[93] B. Widrow and S. Stearns. Adaptive Signal Processing. Prentice Hall, 1985.[94] Z. Xiong, R. Caballero, H. Wang, A. Finn, M. Lelic, and P.-Y. Peng. Video based Smoke <strong>Detection</strong>: Possibilities, Techniques,and Challenges, available at http://vision.ai.uiuc.edu/∼wanghc/papers/smoke_detection.pdf, Accessed at December 2008.[95] D. Youla and H. Webb. Image restoration by the method of convex projections,Part I-Theory. IEEE Transactions on Medical Imaging, MI-I-2:81–94,1982.[96] J. Zhong and S. Sclaroff. Segmenting foreground objects from a dynamictextured background via a robust kalman filter. In Proceedings of the InternationalConference on Computer Vision (ICCV), pages 44–50, 2003.