Shape representation and description using the Hilbert curve

  • Authors:
  • Yasser Ebrahim;Maher Ahmed;Wegdan Abdelsalam;Siu-Cheung Chau

  • Affiliations:
  • Prince Sultan University, Box No. 66833, Riyadh 11586, Saudi Arabia;Wilfrid Laurier University, 75 University Avenue, Waterloo, Ontario, Canada N2L 3C5;Prince Sultan University, Box No. 66833, Riyadh 11586, Saudi Arabia;Wilfrid Laurier University, 75 University Avenue, Waterloo, Ontario, Canada N2L 3C5

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2009

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Abstract

In this paper, a novel linear-time approach to shape representation and description is presented. The object shape is captured by scanning the object image using a space-filling curve (SFC). The resulting vector is smoothed, using wavelet approximation, and sampled. In addition, the concept of key feature points (KFPs) is introduced to utilize a priori information about the classification of the images in the database in optimizing the representation of the objects within each class. The proposed technique achieves a recognition rate of 88.3% on the MPEG-7 core experiment part B. On the Kimia-99 and Kimia-216 datasets, a precision average of 95.6% is attained. Retrieval rates of 94.2% and 95.6% are achieved on the gray-scale and binary versions of the ETH-80 dataset, respectively.