A one-dimension example of scale-space segmentation. A signal (black), multi-scale-smoothed versions of it (red), and segment averages (blue) based on scale-space segmentationThe dendrogram corresponding to the segmentations in the figure above. Each "×" identifies the position of an extremum of the first derivative of one of 15 smoothed versions of the signal (red for maxima, blue for minima). Each "+" identifies the position that the extremum tracks back to at the finest scale. The signal features that persist to the highest scale (smoothest version) are evident as the tall structures that correspond to the major segment boundaries in the figure above.
Scale-space segmentation or multi-scale segmentation is a general framework for signal and image segmentation, based on the computation of image descriptors at multiple scales of smoothing.
