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
- 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 ↗
- Witkin, A. P. (1983). Scale-space filtering. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI), 1019–1022. link ↗