Figure 1. IKONOS satellite image of a lake.

Figure 2. Image tangent map.

Figure 3. Example closed contour.

Bayesian models and algorithms for the perceptual grouping of contours

Our starting point for the development of contour grouping algorithms is the image edge map, attained by application of a multiscale edge detection algorithm. We also make use of the tangent map, in which edgels have been grouped together to form short, linear segments.

See Figures 1, 2 and 3.

We formulate the contour grouping problem as a Bayesian inference task, based upon both photometric and geometric cue likelihood models constructed from the statistics of natural images. This modelling forms the basis of Aaron Clarke’s Master’s project research.

  • Contour grouping with prior models (Elder, Krupnik and Johnston, PAMI 2003)
    In this paper we investigate how prior knowledge may be incorporated in a probabilistic framework for determining the bounding contour of an object of interest. The maximum a posteriori inference problem is computationally intractable, and thus a constructive algorithm is presented which builds up a set of candidate closed contours that have high probability of representing the true object boundary.
  • Efficient computation of closed contours using modified Baum-Welch updating (Johnston and Elder, POCV 2004)
    The incorporation of contour closure is a vital and yet difficult aspect of contour grouping for object boundary determination. We extend the previous constructive algorithm approach by way of a dynamic reweighting scheme that influences contour growth toward closure. The re-weighting is determined by an on-line, sequential version of the Baum-Welch algorithm, used for iterative estimation of hidden Markov model parameters.
  • Multiscale contour grouping
    We are currently investigating multiscale contour representations and the subsequent development of multiscale contour grouping algorithms based on hierarchical hidden Markov models and Belief Propagation.