Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Gaussian Scale-Space Theory
Generic Events for the Gradient Squared with Application to Multi-Scale Segmentation
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
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In order to reconcile linear and morphological scale space an image decomposition based on radial basis functions (RBF's) may be used. Radial-basis functions can be used to synthesize approximations of multidimensional functions, i.e. solving the problem of hypersurface reconstruction thus approximating the image intensities. They can be used in several ways in RBF networks which are linear neural networks. They provide a link between linear and morphological scale spaces. A morphological scale-space is a scale dependent decomposition of images from coarse to fine scale based on morphological operations on images. This is in contrast to the linear scale space, which is based on gaussian smoothing of image features. Both types of scale space are being advocated for image segmentation.