The Hough Transform without the Accumulators

  • Atsushi Imiya
  • Tetsu Hada
  • Ken Tatara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


The least-squares method (LSM) efficiently solves the model-fitting problem, if we assume a model equation. For the fitting to a collection of models, the classification of data is required as pre-processing. The Hough transform, achieves both the classification of sample points and the model fitting concurrently. However, as far as adopting the voting process is concerned, the maintenance of the accumulator during the computation cannot be neglected. We propose a Hough transform without the accumulator expressing the classification of data for the model fitting problems as the permutation of matrices which are defined by data.


Sample Point Orthogonal Matrix Projection Matrix Hough Transform Moment Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Atsushi Imiya
    • 1
    • 2
  • Tetsu Hada
    • 2
  • Ken Tatara
    • 2
  1. 1.National Institute of InformaticsTokyo
  2. 2.IMITChiba UniversityChibaJapan

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