Hierarchical pre-segmentation without prior knowledge

A new method to pre-segment images by means of a hierarchical description is proposed. This description is obtained from an investigation of the deep structure of a scale space image-the input image and the Gaussian filtered ones simultaneously. We concentrate on scale space critical points-points with vanishing gradient with respect to both spatial and scale direction. We show that these points are always saddle points. They turn out to be extremely useful, since the iso-intensity manifolds through these points provide a scale space hierarchy tree and induce a segmentation without a priori knowledge. Moreover, together with the so-called catastrophe points, these scale space saddles form the critical points of the parameterised critical curves-the curves along which the spatial saddle points move in scale space. Experimental results with respect to the hierarchy and segmentation are given, based on an artificial image and a simulated MRI.