Machine learningMulti-scale image analysis

Scale-Space Theory

Scale-space theory, developed by Witkin and Lindeberg, provides a principled mathematical framework for analyzing images at multiple scales simultaneously. By treating scale as an explicit dimension and using Gaussian blurring, scale-space theory enables detection and analysis of features at appropriate scales, solving the fundamental problem of 'which scale should I analyze at?'

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Sources

  1. Lindeberg, T. (1994). Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(2), 225–270. DOI: 10.1080/757582976
  2. Witkin, A. P. (1983). Scale-space filtering. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI), 1019–1022. link

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Referenced by

ScholarGateScale-Space Theory (Scale-Space Theory and Multi-Scale Image Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/computer-vision/scale-space-theory