W.J. Niessen, K.L. Vincken, J. Weickert, M.A. Viergever,
Nonlinear multiscale representations for image segmentation,
Computer Vision and Image Understanding, Vol. 66, 233-245, 1997.
In order to segment an image the use of information at multiple
scale is invaluable. The hyperstack, a linking model based segmentation
technique, uses intensity to link points in adjacent levels of a
scale space stack. This approach has been successfully applied to linear
multiscale representations. Multiscale representations which satisfy
two scale-space properties, viz. a causality and a semigroup property
in differential form, are valid inputs as well. In this paper we
consider linear scale-space, gradient dependent diffusion and the
Euclidean shortening flow. Since no global scale parameter is
available in the latter two approaches we compare scale levels
based on evolution time, information theoretic measures and by
counting the number of objects. The multiscale representations are
compared with respect to their performance in image segmentation
tasks on test and MR images. The hyperstack proofs to be rather
insensitive to the underlying multiscale representation although
the nonlinear representations reduced the number of post processing
steps.
Return to
Joachim Weickert's publication list.