on multi-scale watersheds. The use of multi-scale watershed with application to the gradient map has also been investigated by Olsen and Nielsen and has been carried over to clinical use by Dam et al. Vincken et al. proposed a hyperstack for defining probabilistic relations between image structures at different scales. The use of stable image structures over scales has been furthered by Ahuja and his co-workers into a fully automated system. A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg and been extensively tested in brain databases. These ideas for multi-scale image segmentation by linking image structures over scales have also been picked up by Florack and Kuijper. Bijaoui and Rué associate structures detected in scale-space above a minimum noise threshold into an object tree which spans multiple scales and corresponds to a kind of feature in the original signal. Extracted features are accurately reconstructed using an iterative conjugate gradient matrix method. Segmentation of vector functions of time Scale-space segmentation was extended in another direction by Lyon to vector-valued functions of time, where the vector derivative does not have maxima and minima, and the second derivative does not have zero crossings, by putting segment boundaries instead at maxima of the Euclidean magnitude of the vector derivative of the smoothed vector signals. This technique has been applied to segmentation of speech and of text. References See also segmentation (image processing) scale space Image segmentation