A generalised approach to the use of sampling for rapid object location

E. R. Davies

International Journal of Applied Mathematics and Computer Science (2008)

  • Volume: 18, Issue: 1, page 7-19
  • ISSN: 1641-876X

Abstract

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This paper has developed a generalised sampling strategy for the rapid location of objects in digital images. In this strategy a priori information on the possible locations of objects is used to guide the sampling process, and earlier body-based and edge-based approaches emerge automatically on applying the right a priori probability maps. In addition, the limitations of the earlier regular sampling technique have been clarified and eased - with the result that sampling patterns are better matched to the positions of the image boundaries. These methods lead to improved speeds of operation both in the cases where all the objects in an image have to be located and also where the positions of individual objects have to be updated. Finally, the method is interesting in being intrinsically able to perform full binary search tree edge location without the need for explicit programming.

How to cite

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E. R. Davies. "A generalised approach to the use of sampling for rapid object location." International Journal of Applied Mathematics and Computer Science 18.1 (2008): 7-19. <http://eudml.org/doc/207867>.

@article{E2008,
abstract = {This paper has developed a generalised sampling strategy for the rapid location of objects in digital images. In this strategy a priori information on the possible locations of objects is used to guide the sampling process, and earlier body-based and edge-based approaches emerge automatically on applying the right a priori probability maps. In addition, the limitations of the earlier regular sampling technique have been clarified and eased - with the result that sampling patterns are better matched to the positions of the image boundaries. These methods lead to improved speeds of operation both in the cases where all the objects in an image have to be located and also where the positions of individual objects have to be updated. Finally, the method is interesting in being intrinsically able to perform full binary search tree edge location without the need for explicit programming.},
author = {E. R. Davies},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {machine vision; automated inspection and assembly; rapid object location; sampling techniques},
language = {eng},
number = {1},
pages = {7-19},
title = {A generalised approach to the use of sampling for rapid object location},
url = {http://eudml.org/doc/207867},
volume = {18},
year = {2008},
}

TY - JOUR
AU - E. R. Davies
TI - A generalised approach to the use of sampling for rapid object location
JO - International Journal of Applied Mathematics and Computer Science
PY - 2008
VL - 18
IS - 1
SP - 7
EP - 19
AB - This paper has developed a generalised sampling strategy for the rapid location of objects in digital images. In this strategy a priori information on the possible locations of objects is used to guide the sampling process, and earlier body-based and edge-based approaches emerge automatically on applying the right a priori probability maps. In addition, the limitations of the earlier regular sampling technique have been clarified and eased - with the result that sampling patterns are better matched to the positions of the image boundaries. These methods lead to improved speeds of operation both in the cases where all the objects in an image have to be located and also where the positions of individual objects have to be updated. Finally, the method is interesting in being intrinsically able to perform full binary search tree edge location without the need for explicit programming.
LA - eng
KW - machine vision; automated inspection and assembly; rapid object location; sampling techniques
UR - http://eudml.org/doc/207867
ER -

References

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  10. Davies E.R. (2001). A sampling approach to ultra-fast object location, Real-Time Imaging 7(4): 339-355. Zbl0972.68717
  11. Davies E.R. (2003). Design of real-time algorithms for food and cereals inspection, Imaging Science 51(2): 63-78. 
  12. Davies E.R. (2005). Machine Vision: Theory, Algorithms, Practicalities, 3rd, Ed. Morgan Kaufmann, San Francisco. 
  13. Davies E.R. (2007). Guided sampling for rapid object location using biologically motivated model, Electronics Letters 43(9): 508-510. 
  14. Itti L., Koch C. and Niebur E. (1998). A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11): 1254-1259. 
  15. Nagel R.N. and Rosenfeld A. (1972). Ordered search techniques in template matching, Proceedings IEEE 60(2), 242-244. 
  16. Palmer S.E. (1999). Visual selection: Eye movements and attention, In S.E. Palmer, (Vision Science: Photons to Phenomenology), Bradford Books/MIT Press, Cambridge, MA, pp. 519-571. 
  17. Rosenfeld A. and VanderBrug G.J. (1977a). Coarse-fine template matching, IEEE Transactions on Systems, Man and Cybernetics 7(4): 104-107. Zbl0357.68102
  18. VanderBrug G.J. and Rosenfeld A. (1977b). Two-stage template matching, IEEE Transactions on Computers 26(2): 384-393. Zbl0357.68102

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