Probabilistic framework for grouping image features

Rebecca L. Castano, Seth Hutchinson

Research output: Contribution to conferencePaperpeer-review


We present a framework for determining probability distributions over the space of possible image feature groupings. Such a framework allows higher level processes to reason over many plausible perceptual groupings in an image, rather than committing to a specific image segmentation in the early stages of processing. We first derive an expression for the probability that a set of features should be grouped together, conditioned on the observed image data associated with those features. This probability measure formalizes the principle that features in an image should be grouped together when they participate in a common underlying geometric structure. We then present a representation scheme in which only those groupings with high probability are explicitly represented, while large sets of unlikely grouping hypotheses are implicitly represented. We present experimental results for a variety of real intensity images.

Original languageEnglish (US)
Number of pages6
StatePublished - 1995
Externally publishedYes
EventInternational Symposium on Computer Vision, ISCV'95, Proceedings - Coral Gables, FL, USA
Duration: Nov 21 1995Nov 23 1995


OtherInternational Symposium on Computer Vision, ISCV'95, Proceedings
CityCoral Gables, FL, USA

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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