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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Alzarok, Hamza; Fletcher, Simon; Longstaff, Andrew P.; Myers, Alan (2015)
Publisher: EUSPEN
Languages: English
Types: Part of book or chapter of book
Subjects: TJ, TS

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective.\ud This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baker, S. and T. Kanade. Hallucinating faces. in Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. 2000.
    • Chaudhuri, S. and J. Manjunath, Motion-free Super-resolution2006: Springer.
    • Yang, J. and T. Huang, Image super-resolution: Historical overview and future challenges. Super-resolution imaging, 2010: p. 20-34.
    • Park, S.C., M.K. Park, and M.G. Kang, Super-resolution image reconstruction: a technical overview. Signal Processing Magazine, IEEE, 2003. 20(3): p. 21-36.
    • Chaudhuri, S., Super-resolution imaging2001: Springer.
    • Grothe, B. and T. Park, Structure and function of the bat superior olivary complex. Microscopy Research and Technique, 2000. 51(4): p.
    • Yilmaz, A., O. Javed, and M. Shah, Object tracking: A survey. Acm computing surveys (CSUR), 2006. 38(4): p. 13.
    • Nüchter, A., 3D robotic mapping: the simultaneous localization and mapping problem with six degrees of freedom. Vol. 52. 2009: Springer Science & Business Media.
    • Ramey, A., V. González-Pacheco, and M.A. Salichs. Integration of a low-cost RGB-D sensor in a social robot for gesture recognition. in Proceedings of the 6th international conference on Human-robot interaction. 2011. ACM.
    • Cunha, J., et al., Using a depth camera for indoor robot localization and navigation. DETI/IEETA-University of Aveiro, Portugal, 2011.
    • Bannore, V., Iterative-interpolation super-resolution (iisr), in IterativeInterpolation Super-Resolution Image Reconstruction2009, Springer.
    • p. 19-50.
    • Katsaggelos, A.K., R. Molina, and J. Mateos, Super resolution of images and video. Synthesis Lectures on Image, Video, and Multimedia Processing, 2007. 1(1): p. 1-134.
    • Tsai, R. and T.S. Huang, Multiframe image restoration and registration. Advances in computer vision and Image Processing, 1984. 1(2): p. 317-339.
    • Negroponte, N., Being digital1996: Random House LLC.
    • Alvarez, L.D., R. Molina, and A.K. Katsaggelos, High resolution images from a sequence of low resolution observations. Digital Image Sequence Processing, Compression and Analysis, 2004. 9: p. 233-259.
    • Stark, H. and P. Oskoui, High-resolution image recovery from imageplane arrays, using convex projections. JOSA A, 1989. 6(11): p. 1715- 1726.
    • Caner, G., A.M. Tekalp, and W. Heinzelman. Super resolution recovery for multi-camera surveillance imaging. in Multimedia and 18.
    • Expo, 2003. ICME'03. Proceedings. 2003 International Conference on. 2003. IEEE.
    • Elad, M. and A. Feuer, Superresolution restoration of an image sequence: adaptive filtering approach. Image Processing, IEEE Transactions on, 1999. 8(3): p. 387-395.
    • Elad, M. and A. Feuer. Super-resolution reconstruction of continuous image sequences. in Image Processing, 1999. ICIP 99. Proceedings.
    • 1999 International Conference on. 1999. IEEE.
    • Elad, M. and A. Feuer, Super-resolution reconstruction of image sequences. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1999. 21(9): p. 817-834.
    • CVGIP: Graphical models and image processing, 1991. 53(3): p. 231- 239.
    • Baker, S. and T. Kanade, Limits on super-resolution and how to break them. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(9): p. 1167-1183.
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