Abstract
Contrast media is a kind of chemical substance used to improve the image quality of Computed Tomography. However, due to its high speed of injection, emergencies (such as capillary hemorrhage) always exist. In view of this problem, a video object tracking system is implemented to monitor the injection site. The color feature is abstracted from image sequences and used for the mean shift tracking algorithm. The experiment results show that the tracking system is real-time, robust and efficient.
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References
Comanniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Hager, G.D., Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(10), 1025–1039 (1998)
Tissainayagam, P., Suter, D.: Object tracking in image sequences using point feature. Pattern Recognition 38(1), 105–113 (2005)
Coifmana, B., Beymerb, D., McLauchlanb, P., Malikb, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research, 271–288 (1998)
Clady, X., Collunge, F., Jurie, F., Martinet, P.: Object Tracking with a Pan-Tilt-Zoom Camera:application to car driving assistance. In: Proceedings of the IEEE International Conference on Robotics & Automation, pp. 1653–1658 (February 2001)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. O’Reilly Media, Sebastopol (2008)
David, M., Tourahimi, E.: Orientation histogram-based matching for region tracking. In: Eight International Workshop on Image Analysis for Multimedia Interactive Services, pp. 6–8 (2007)
Xiehua, Z., Shen, Z., Min, T.: Research on the ‘Selective Color Histogram’ for Moving Object Tracking. Journal of Chinese Computer Systems 30, 1864–1868 (1998)
Yuan, X.: Tracking Moving People Based on the MeanShift Algorithm. Computer Engineering & Science 30(4), 46–49 (2008)
Han, H., Zhi, J.W., Jiao, L.C., Chen, Z.P.: Data Association for Multiple Targets Based on MeanShift in Clutter. Journal of System Simulation 21(11), 3351–3355 (2009)
Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal Q2, 1–15 (1998)
Yuan, F.N.: A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recognition Letters 29, 925–932 (2008)
Marimon, D., Ebrahimi, T.: Orientation histogram-based matching for region tracking. In: Eight International Workshop on Image Analysis for Multimedia Interactive Services, pp. 6–8 (June2007)
Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine Vision and Applications 14(1), 50–58 (2003)
Wang, H., Wang, J.T., Ren, M.W., Yang, J.Y.: A New Robust Object Tracking Algorithm by Fusing Multi-features. Journal of Image and Graphics 14(3), 489–498 (2009)
Sanjeev Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Process. 50(2), 174–188 (2002)
Mokhtarian, F., Suomela, R.: Robust Image Corner Detection Through Curvature Scale Space. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1376–1381 (1998)
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Dai, C., Wang, Z., Zhao, H. (2010). A Medical Tracking System for Contrast Media. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_61
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DOI: https://doi.org/10.1007/978-3-642-15615-1_61
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