Multiple resolution search techniques for the hough transform in high dimensional parameter spaces

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

The standard Hough transform could be used to solve recognition tasks in range data, if it were not for the high dimensional parameter spaces these tasks require. This paper describes two alternative methods which apply search techniques at multiple resolutions in order to find sets of parameters which fit the data best. Both methods are as robust as but much faster than the standard Hough transform. The first method, called recursive lattice search, employs a data structure similar to the quad-tree or oct-tree in order to attain its efficiency. The second method, called resolution hill climbing, finds a trail of hypothesized parameter sets, each of which fits the data at a higher resolution than the one preceding it. An example two dimensional recognition task and an example three dimensional recognition task are considered both without and with gradient information. Finally, a more abstract type of feature, the reflection symmetry, is shown to be recognizable by a modification of the first method. Some results of an implementation of the two methods are described.

Original languageEnglish (US)
Title of host publicationMachine Intelligence and Pattern Recognition
Pages231-254
Number of pages24
EditionC
DOIs
StatePublished - Jan 1986
Externally publishedYes

Publication series

NameMachine Intelligence and Pattern Recognition
NumberC
Volume3
ISSN (Print)0923-0459

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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  • Cite this

    Milenkovic, V. J. (1986). Multiple resolution search techniques for the hough transform in high dimensional parameter spaces. In Machine Intelligence and Pattern Recognition (C ed., pp. 231-254). (Machine Intelligence and Pattern Recognition; Vol. 3, No. C). https://doi.org/10.1016/B978-0-444-87901-1.50012-4