A geometrical framework for low level vision (1998)
| Venue: | IEEE Trans. on Image Processing |
| Citations: | 131 - 28 self |
BibTeX
@ARTICLE{Sochen98ageometrical,
author = {Nir Sochen and Ron Kimmel and Ravikanth Malladi},
title = {A geometrical framework for low level vision},
journal = {IEEE Trans. on Image Processing},
year = {1998},
pages = {310--318}
}
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Abstract
Abstract—We introduce a new geometrical framework based on which natural flows for image scale space and enhancement are presented. We consider intensity images as surfaces in the space. The image is, thereby, a two-dimensional (2-D) surface in three-dimensional (3-D) space for gray-level images, and 2-D surfaces in five dimensions for color images. The new formulation unifies many classical schemes and algorithms via a simple scaling of the intensity contrast, and results in new and efficient schemes. Extensions to multidimensional signals become natural and lead to powerful denoising and scale space algorithms. Index Terms — Color image processing, image enhancement, image smoothing, nonlinear image diffusion, scale-space. I.
