From high energy physics to low level vision

Ron Kimmel, Nir Sochen, Ravi Malladi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A geometric framework for image scale space, enhancement, and segmentation is presented. We consider intensity images as surfaces in the (x, I) space. The image is thereby a 2D surface in 3D space for gray level images, and a 2D surface in 5D 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 multi dimensional signals become natural and lead to powerful denoising and scale space algorithms. Here, we demonstrate the proposed framework by applying it to denoise and improve gray level and color images.

Original languageEnglish
Title of host publicationScale-Space Theory in Computer Vision - 1st International Conference, Scale-Space 1997, Proceedings
EditorsBart ter Haar Romeny, Max Viergever, Luc Florack, Jan Koenderink
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540631674, 9783540631675
StatePublished - 1997
Event1st International Conference on Scale-Space Theory in Computer Vision, Scale-Space 1997 - Utrecht, Netherlands
Duration: 2 Jul 19974 Jul 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Scale-Space Theory in Computer Vision, Scale-Space 1997


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