A Generalised Approach to the use of Sampling for Rapid Object Location

E. Davies 1
  • 1 Machine Vision Group, Department of Physics Royal Holloway, University of London, Egham Surrey, TW20 0EX, UK

A Generalised Approach to the use of Sampling for Rapid Object Location

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.

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  • Ballard D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition 13(2): 111-122.

  • Davies E.R. (1987a). A high speed algorithm for circular object location, Pattern Recognition Letters 6(5): 323-333.

  • Davies E.R. (1987b). Lateral histograms for efficient object location: Speed versus ambiguity, Pattern Recognition Letters 6(3): 189-198.

  • Davies E.R. (1988). A modified Hough scheme for general circle location, Pattern Recognition Letters 7(1): 37-43.

  • Davies E.R. (1997). Lower bound on the processing required to locate objects in digital images, Electronics Letters 33(21): 1773-1774.

  • Davies E.R. (1998). Rapid location of convex objects in digital images, Proceedings of the European Signal and Image Processing Conference (EUSIPCO'98), Rhodes, Greece, pp. 589-592.

  • Davies E.R. (1999). Algorithms for ultra-fast location of ellipses in digital images, Proceedings of 7th IEE Int. Conference on Image Processing and Its Applications, Manchester, UK, pp. 542-546.

  • Davies E.R. (2000a). Low-level vision requirements, Electronics and Communication Engineering Journal 12(5): 197-210.

  • Davies E.R. (2000b). Image Processing for the Food Industry, World Scientific, Singapore.

  • Davies E.R. (2001). A sampling approach to ultra-fast object location, Real-Time Imaging 7(4): 339-355.

  • Davies E.R. (2003). Design of real-time algorithms for food and cereals inspection, Imaging Science 51(2): 63-78.

  • Davies E.R. (2005). Machine Vision: Theory, Algorithms, Practicalities, 3rd, Ed. Morgan Kaufmann, San Francisco.

  • Davies E.R. (2007). Guided sampling for rapid object location using biologically motivated model, Electronics Letters 43(9): 508-510.

  • 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.

  • Nagel R.N. and Rosenfeld A. (1972). Ordered search techniques in template matching, Proceedings IEEE 60(2), 242-244.

  • 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.

  • Rosenfeld A. and VanderBrug G.J. (1977a). Coarse-fine template matching, IEEE Transactions on Systems, Man and Cybernetics 7(4): 104-107.

  • VanderBrug G.J. and Rosenfeld A. (1977b). Two-stage template matching, IEEE Transactions on Computers 26(2): 384-393.


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