Elsevier

Pattern Recognition

Volume 25, Issue 9, September 1992, Pages 987-1006
Pattern Recognition

Qualitative features and the generalized hough transform

https://doi.org/10.1016/0031-3203(92)90063-OGet rights and content

Abstract

In this paper we show how the use of qualitative features can enhance the performance of recognition and localization techniques, in particular, the Generalized Hough Transform. Qualitative features (i.e. scene features with qualitative attributes assigned to them) are shown to be effective in pruning the search space of possible scene interpretations and also reducing the number of spurious interpretations explored by the recognition and localization technique. The redundancy of the computed transform and the probability of spurious peaks of significant magnitude due to random accumulation of evidence are two criteria by which the performance of the Generalized Hough Transform is judged. The straightforward Generalized Hough Transform shows a high probability of spurious peaks of significant magnitude even for small values of redundancy and small magnitude of the search space of scene interpretations. The use of qualitative features enables us to come up with a weighted Generalized Hough Transform where each match of a scene feature with a model feature is assigned a weight based on the qualitative attributes assigned to the scene feature. These weights could be looked upon as membership function values for the fuzzy sets defined by these qualitative attributes. Analytic expressions for the probability of accumulation of random events within a bucket are derived for the weighted Generalized Hough Transform and compared with the corresponding expression for the straightforward Generalized Hough Transform. The weighted Generalized Hough Transform is shown to perform better than the straightforward Generalized Hough Transform. An experiment for the recognition of polyhedral objects from range images is described using dihedral junctions as features for matching and pose computation. The experimental results bring out the advantages of the weighted Generalized Hough Transform over the straightforward Generalized Hough Transform.

References (45)

  • W.E.L. Grimson

    The combinatorics of object recognition in cluttered environments using constrained search

    Artif. Intell.

    (1990)
  • L.A. Zadeh

    Probability measures of fuzzy events

    J. Math. Analysis Applic.

    (1968)
  • R.M. Capocelli et al.

    Fuzzy sets and decision theory

    Inf. Control

    (1973)
  • S.M. Bhandarkar et al.

    Sensitivity analysis for matching and pose computation using dihedral junctions

    Pattern Recognition

    (1991)
  • S.M. Bhandarkar et al.

    Pose verification as an optimal assignment problem

    Pattern Recognition Lett.

    (1991)
  • D. Marr

    Vision

    (1982)
  • D. Lowe

    Perceptual Organization and Visual Recognition

    (1985)
  • B. Julesz et al.

    Textons—the fundamental elements in preattentive vision and the perception of textures

    Bell Syst. Tech. J.

    (July/August 1983)
  • D. Hoffman et al.

    Parts of recognition

    Cognition

    (1985)
  • A. Guzman

    Computer recognition of three-dimensional objects in a visual scene

  • D. Waltz

    Understanding line drawings of scenes with shadows

  • J. Malik

    Interpreting line drawings of curved objects

    Int. J. Comput. Vision

    (1987)
  • Cited by (0)

    View full text